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Owing to probabilistic uncertainties associated with seasonal forecasts, especially over areas such as southern Africa where forecast skill is limited, non-climatologists and users of such forecasts frequently prefer them to be presented or distributed in terms of the likelihood (expressed as a probability) of certain categories occurring or thresholds being exceeded. Probabilistic forecast verification is needed to verify such forecasts. Whilst the resulting verification statistics can provide clear insights into forecast attributes, they are often difficult to understand, which might hinder forecast uptake and use. This problem can be addressed by issuing forecasts with some understandable evidence of skill, with the purpose of reflecting how similar forecasts may have performed in the past. In this paper, we present a range of different probabilistic forecast verification scores, and determine if these statistics can be readily compared to more commonly known and understood ‘ordinary’ correlations between forecasts and their associated observations – assuming that ordinary correlations are more intuitively understood and informative to seasonal forecast users. Of the range of scores considered, the relative operating characteristics (ROC) was found to be the most intrinsically similar to correlation. [ABSTRACT FROM AUTHOR]

In this paper we exemplify the types of Plithogenic Probability and respectively Plithogenic Statistics. Several applications are given. The Plithogenic Probability of an event to occur is composed from the chances that the event occurs with respect to all random variables (parameters) that determine it. Each such a variable is described by a Probability Distribution (Density) Function, which may be a classical, (T,I,F)-neutrosophic, I-neutrosophic, (T,F)-intuitionistic fuzzy, (T,N,F)-picture fuzzy, (T,N,F)-spherical fuzzy, or (other fuzzy extension) distribution function. The Plithogenic Probability is a generalization of the classical MultiVariate Probability. The analysis of the events described by the plithogenic probability is the Plithogenic Statistics. [ABSTRACT FROM AUTHOR]

In these cases getting accurate confidence intervals is of first order importance, and the methods Ignatiadis and Wager develop are likely to be useful. I want to congratulate Nikolaos Ignatiadis and Stefan Wager on a very stimulating paper on a timely and important topic. [Extracted from the article]

Context: Self-report questionnaires are an important method of evaluating lifespan health, exercise, and health-related quality of life (HRQL) outcomes among elite, competitive athletes. Few instruments, however, have undergone formal characterization of their psychometric properties within this population. Objective: To evaluate the validity and reliability of a novel health and exercise questionnaire, the Trojan Lifetime Champions (TLC) Health Survey. Design: Descriptive laboratory study. Setting: A large National Collegiate Athletic Association Division I university. Patients or Other Participants: A total of 63 university alumni (age range, 24 to 84 years), including former varsity collegiate athletes and a control group of nonathletes. Intervention(s): Participants completed the TLC Health Survey twice at a mean interval of 23 days with randomization to the paper or electronic version of the instrument. Main Outcome Measure(s): Content validity, feasibility of administration, test-retest reliability, parallel-form reliability between paper and electronic forms, and estimates of systematic and typical error versus differences of clinical interest were assessed across a broad range of health, exercise, and HRQL measures. Results: Correlation coefficients, including intraclass correlation coefficients (ICCs) for continuous variables and K agreement statistics for ordinal variables, for test-retest reliability averaged 0.86, 0.90, 0.80, and 0.74 for HRQL, lifetime health, recent health, and exercise variables, respectively. Correlation coefficients, again ICCs and K, for parallel-form reliability (ie, equivalence) between paper and electronic versions averaged 0.90, 0.85, 0.85, and 0.81 for HRQL, lifetime health, recent health, and exercise variables, respectively. Typical measurement error was less than the a priori thresholds of clinical interest, and we found minimal evidence of systematic testretest error. We found strong evidence of content validity, convergent construct validity with the Short-Form 12 Version 2 HRQL instrument, and feasibility of administration in an elite, competitive athletic population. Conclusions: These data suggest that the TLC Health Survey is a valid and reliable instrument for assessing lifetime and recent health, exercise, and HRQL, among elite competitive athletes. Generalizability of the instrument may be enhanced by additional, larger-scale studies in diverse populations. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups show a significant difference in reliability, with the observation that the outcomes depend on whether the vehicles are operated by developers or nondevelopers. This paper shows that this difference in reliability is due to the failure prevention and correction procedures--risk mitigation--put in place by developers. However, no formalization has been developed for updating the risk profile based on the expected effectiveness of the failure prevention and correction process. A generic Bayesian approach for updating the risk profile is presented, based on the probability of failure prevention and correction and the number of subsequent deployments on which the failure does not occur. The approach, which applies whether the risk profile is captured in a parametric or nonparametric survival model, is applied to a real case study of the International Submarine Engineering Ltd. (ISE) Explorer AUV. [ABSTRACT FROM AUTHOR]

Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practical applications. This paper presents an adaptive Gaussian mixture PHD (AGM-PHD) filter for the multi-target tracking (MTT) problem in the scenario where both the mean and covariance of measurement noise sequences are unknown. The conventional PHD filters are extended to jointly estimate both the multi-target state and the aforementioned measurement noise statistics. In particular, the Normal-inverse-Wishart and Gaussian distributions are first integrated to represent the joint posterior intensity by transforming the measurement model into a new formulation. Then, the updating rule for the hyperparameters of the model is derived in closed form based on variational Bayesian (VB) approximation and Bayesian conjugate prior heuristics. Finally, the dynamic system state and the noise statistics are updated sequentially in an iterative manner. Simulations results with both constant velocity and constant turn model demonstrate that the AGM-PHD filter achieves comparable performance as the ideal PHD filter with true measurement noise statistics. [ABSTRACT FROM AUTHOR]

Matrix completion algorithms recover a low rank matrix from a small fraction of the entries, each entry contaminated with additive errors. In practice, the singular vectors and singular values of the low rank matrix play a pivotal role for statistical analyses and inferences. This paper proposes estimators of these quantities and studies their asymptotic behavior. Under the setting where the dimensions of the matrix increase to infinity and the probability of observing each entry is identical, Theorem 1 gives the rate of convergence for the estimated singular vectors; Theorem 3 gives a multivariate central limit theorem for the estimated singular values. Even though the estimators use only a partially observed matrix, they achieve the same rates of convergence as the fully observed case. These estimators combine to form a consistent estimator of the full low rank matrix that is computed with a non-iterative algorithm. In the cases studied in this paper, this estimator achieves the minimax lower bound in Koltchinskii, Lounici and Tsybakov (2011). The numerical experiments corroborate our theoretical results. [ABSTRACT FROM AUTHOR]

Background The relationship between HIV and poverty is complex and recent studies reveal an urban--rural divide that is not well understood. This paper examines the urban--rural disparity in the relationship between poverty and HIV infection in Kenya, with particular reference to possible explanations relating to social cohesion/capital and other moderating factors. Methods Multilevel logistic regression models are applied to nationally-representative samples of 13 094 men and women of reproductive age from recent Kenya Demographic and Health Surveys. Results The results confirm a disproportionate higher risk of HIV infection among the urban poor, despite a general negative association between poverty and HIV infection among rural residents. Estimates of intra-community correlations suggest lower social cohesion in urban than rural communities. This, combined with marked socio-economic inequalities in urban areas is likely to result in the urban poor being particularly vulnerable. The results further reveal interesting cultural variations and trends. In particular, recent declines in HIV prevalence among urban residents in Kenya have been predominantly confined to those of higher socio-economic status. Conclusion With current rapid urbanization patterns and increasing urban poverty, these trends have important implications for the future of the HIV epidemic in Kenya and similar settings across the sub-Saharan Africa region. [ABSTRACT FROM AUTHOR]

The use of technology in teaching and learning the topic of probability and statistics in the educational system in our country, Albania, has the potential to be encouraged in different level of classes in the educational system. Lee and Hollebrands (2011) have done better characterization of teaching probability and statistics with the help of technology, through the integration of Statistical Knowledge, Technological Statistical Knowledge, and Technological Pedagogical Statistical Knowledge. Also, we have integrated this framework with Pedagogical Knowledge as a very important factor. In this paper is given as a case study, the process of mastering the concepts of probability and statistics, with students of a lower secondary school in our country to understand the role of technology in the process of teaching and learning, realized concretely during the lessons, specifically throughout a project-based learning by demonstrating the results of the project by the students themselves. Our students recognize and analyze the data, then collect and represent it, to provide graphical presentation Thus, we focus on the potential advantages of using technology in teaching and learning the topics of probability and statistics in school mathematics during different levels of educational process in our country and beyond. [ABSTRACT FROM AUTHOR]

INTRODUCTION While tobacco Quitlines are effective in the promotion of smoking cessation, the majority of callers who wish to quit still fail to do so. The aim of this study was to determine if 12-month tobacco Quitline smoking cessation rates could be improved with re-engagement of callers whose first Quitline treatment failed to establish abstinence. METHODS In an adaptive trial, 614 adult smokers, who were active duty, retired, and family of military personnel with TRICARE insurance who called a tobacco Quitline, received a previously evaluated and efficacious four-session tobacco cessation intervention with nicotine replacement therapy (NRT). At the scheduled follow-up at 3 months, callers who had not yet achieved abstinence were offered the opportunity to re-engage. This resulted in three caller groups: 1) those who were abstinent, 2) those who were still smoking but willing to re-engage with an additional Quitline treatment; and 3) individuals who were still smoking but declined re-engagement. A propensity score-adjusted logistic regression model was generated to compare past-7-day point prevalence abstinence at 12 months post Quitline consultation. RESULTS Using a propensity score adjusted logistic regression model, comparison of the three groups resulted in higher odds of past-7-day point prevalence abstinence at follow-up at 12 months for those who were abstinent at 3 months compared to those who re-engaged (OR=9.6; 95% CI: 5.2-17.8; Bonferroni adjusted p<0.0001), and relative to those who declined re-engagement (OR=13.4; 95% CI: 6.8-26.3; Bonferroni adjusted p<0.0001). There was no statistically significant difference in smoking abstinence between smokers at 3 months who re-engaged and those who declined re-engagement (OR=1.39; 95% CI: 0.68-2.85). CONCLUSIONS Tobacco Quitlines seeking to select a single initiative by which to maximize abstinence at follow-up at 12 months may benefit from diverting additional resources from the re-engagement of callers whose initial quit attempt failed, toward changes which increase callers' probability of success within the first 3 months of treatment. TRIAL REGISTRATION This study is registered at clinicaltrials.gov (NCT02201810). [ABSTRACT FROM AUTHOR]

Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models. [ABSTRACT FROM AUTHOR]

Purpose: Diagnostic classification models (DCMs) were developed to identify the mastery or non-mastery of the attributes required for solving test items, but their application has been limited to very low-level attributes, and the accuracy and consistency of high-level attributes using DCMs have rarely been reported compared with classical test theory (CTT) and item response theory models. This paper compared the accuracy of high-level attribute mastery between deterministic inputs, noisy "and" gate (DINA) and Rasch models, along with sub-scores based on CTT. Methods: First, a simulation study explored the effects of attribute length (number of items per attribute) and the correlations among attributes with respect to the accuracy of mastery. Second, a real-data study examined model and item fit and investigated the consistency of mastery for each attribute among the 3 models using the 2017 Korean Medical Licensing Examination with 360 items. Results: Accuracy of mastery increased with a higher number of items measuring each attribute across all conditions. The DINA model was more accurate than the CTT and Rasch models for attributes with high correlations (>0.5) and few items. In the real-data analysis, the DINA and Rasch models generally showed better item fits and appropriate model fit. The consistency of mastery between the Rasch and DINA models ranged from 0.541 to 0.633 and the correlations of person attribute scores between the Rasch and DINA models ranged from 0.579 to 0.786. Conclusion: Although all 3 models provide a mastery decision for each examinee, the individual mastery profile using the DINA model provides more accurate decisions for attributes with high correlations than the CTT and Rasch models. The DINA model can also be directly applied to tests with complex structures, unlike the CTT and Rasch models, and it provides different diagnostic information from the CTT and Rasch models. [ABSTRACT FROM AUTHOR]

Objective: A point prevalence survey was conducted across Western Australia to monitor adherence to evidence-based practices to prevent falls in hospitals. Study design and methods: A state-wide point prevalence survey of patients and their medical records was conducted across 20 hospitals, over 17 days during May 2014. The survey determined rates of: provision of verbal information to patients; completion of a falls risk screening tool and age based cognitive testing. Univariate and multivariate logistic regression was utilised to determine key risks and opportunities to improve. Results: Information was collected from 2,720 patients. The provision of verbal information to prevent falls, as recalled by patients was 60% (hospital range 35--88%). This was significantly higher for patients with a stay of six or more days or involved in rehabilitation care. Perinatal women were three times less likely to be provided with verbal falls prevention information. A falls risk screening tool was completed for 82% of patients (range 28-98%). Perinatal women, and both adult and paediatric patients compared to older adults, were significantly less likely to have a complete falls risk screening tool. Thirty seven percent of patients within the recommended age ranges had cognitive testing (range 0--87%). Short-term patients and those not involved in rehabilitation, were significantly less likely to have been tested. Discussion: The survey identified differences in patient care and supporting processes across all hospitals. The results have highlighted areas for improvement. Conclusion: There were wide variations across all the hospitals in the provision of falls information, completion of falls risk screening tools and cognitive testing. At significant risk of missing out on falls prevention strategies were short stay patients and perinatal women. Five hospitals had significantly low rates of cognitive testing, indicating a hospital- wide issue rather than specific patient cohorts. Subsequently, the importance of ensuring that falls prevention strategies are conducted is vital to reduce preventable inpatient falls in all care settings. Implications for research, policy and practice: * This was the first state-wide point prevalence study in WA and it has informed the need for further research into the implication of falls risk inpatients. * It was found that falls risk assessment was not conducted for each patient who met the screening criteria. A review of the criteria, and practicability to carry out the assessment may need to be further investigated to determine if the practice should be refined. What is already known about the topic? * Falls in hospitals are a frequent and largely considered preventable health concern. * Falls that occur in hospitals are associated with an increased length of stay and use of health resources. What this paper adds: * This paper offers a comprehensive insight into the variation in hospital falls prevention strategies, from a state-wide perspective. It also identifies perinatal women as a high-risk group who are missing out on falls prevention strategies despite having the potential to fall. * It also gives an insight to health services that not all at risk patients are being screened and those screened are not screened early in their inpatient stay which can be a risk to both patient and staff. [ABSTRACT FROM AUTHOR]

Markov models provide a good approximation to probabilities associated with many categorical time series, and thus they are applied extensively. However, a major drawback associated with them is that the number of model parameters grows exponentially in the order of the model, and thus only very low-order models are considered in applications. Another drawback is lack of flexibility, in that Markov models give relatively few choices for the number of model parameters. Sparse Markov models are Markov models with conditioning histories that are grouped into classes such that the conditional probability distribution for members of each class is constant. The model gives a better handling of the trade-off between bias associated with having too few model parameters and variance from having too many. In this paper, methodology for efficient computation of pattern distributions through Markov chains with minimal state spaces is extended to the sparse Markov framework. [ABSTRACT FROM AUTHOR]

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/. [ABSTRACT FROM AUTHOR]

This special issue is focused on how a Bayesian approach to estimation, inference, and reasoning in organizational research might supplement-and in some cases supplant-—traditional frequentist approaches. Bayesian methods are well suited to address the increasingly complex phenomena and problems faced by 21st-century researchers and organizations, where very complex data abound and the validity of knowledge and methods are often seen as contextually driven and constructed. Traditional modeling techniques and a frequentist view of probability and method are challenged by this new reality. [ABSTRACT FROM AUTHOR]

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This paper has the following aims: (i) to explore the extent to which students who received an intervention involving HIV/AIDS and sexuality perceived that their teacher cared for their health and well-being, (ii) to investigate the characteristics of students who reported to have caring teachers and (iii) to document the association between students’ perceptions of care and reported onset of sexual activity. Data were obtained from the second follow-up survey of a prospective study carried out among high school students in South Africa (Cape Town and Mankweng). We analyzed data from 3483 students who met the inclusion criteria. Students from the intervention group perceived greater care from teachers than students in the control group. Female students and students from Cape Town perceived having received more care, and their perception of care was associated with the number of lessons received, how often students expressed their opinions in class and how often teachers talked about HIV/AIDS, condoms and abstinence. Students who perceived that their teacher cared for their health and well-being were less likely to initiate sexual intercourse. This is the first paper to demonstrate the salience of the concept of care in studies of school-based HIV/AIDS prevention programs in sub-Saharan Africa. [ABSTRACT FROM PUBLISHER]

This paper considers the statistical performance of the MUSIC method under the condition that two closely spaced sources impinging on an array of sensors are effectively resolved, i.e., the spectrum exhibits two peaks in the neighborhood of the true directions-of-arrival (DOA). The MUSIC algorithm is known to have an infinite resolution power in theory. However, in the presence of modeling errors, sources can not be resolved with certainty, even if the array correlation matrix is perfectly known. The focus of this paper is to predict the bias and variance of the DOA estimates taking into account the possible resolution failure of MUSIC. This performance prediction, based on our recent mathematical investigation, is new to the best of our knowledge. A general mathematical framework to derive closed form expressions of the bias and variance versus the model mismatch, conditioned on a general statistical resolution test is proposed. In order to illustrate our mathematical approach, statistical tests with one and two conditions, respectively, are investigated. The accuracy of the performance prediction is illustrated in a simulation study. It is found that the proposed approach outperforms the "classical" technique, which ignores the possible resolution failure of the MUSIC algorithm. Therefore, our results provide better tools for determining the necessary antenna calibration accuracy to achieve some targeted specifications on the estimator performance. [ABSTRACT FROM AUTHOR]

A random variable with distribution P is observed in Gaussian noise and is estimated by a mismatched minimum meansquare estimator that assumes that the distribution is Q, instead of P. This paper shows that the integral over all signal-to-noise ratios (SNRs) of the excess mean-square estimation error incurred by the mismatched estimator is twice the relative entropy D(P||Q) (in nats). This representation of relative entropy can be generalized to nonreal-valued random variables, and can be particularized to give new general representations of mutual information in terms of conditional means. Inspired by the new representation, we also propose a definition of free relative entropy which fills a gap in, and is consistent with, the literature on free probability. [ABSTRACT FROM AUTHOR]

Improving power of goodness-of-fit tests is an important research topic in statistics. The goal of the goodness-of-fit test is to check whether the underlying probability distribution, from which a sample is drawn, differs from a hypothesized distribution. Numerous research papers have been published in this area. It has been shown that the power of the existing goodness-of-fit tests in the literature is unsatisfactory when the alternative distributions are of V-shape or when the sample sizes are small. This motivates the development of more powerful test statistics. In this research, a new test statistic is proposed. The result can be used to test whether the underlying probability distribution differs from a uniform distribution. By applying the probability integral transformation, the proposed test statistic can be used to check whether the underlying distribution differs from any hypothesized distribution. The performance of the method proposed in this research is compared with the Kolmogorov–Smirnov test, which is a widely adopted statistical test in the literature. It has been shown that the test proposed in this proposal is more powerful than the Kolmogorov–Smirnov test in some cases. [ABSTRACT FROM AUTHOR]

Background: Iron metabolism and immune response to SARS-CoV-2 have not been described yet in intensive care patients, although they are likely involved in Covid-19 pathogenesis. Methods: We performed an observational study during the peak of pandemic in our intensive care unit, dosing D-dimer, C-reactive protein, troponin T, lactate dehydrogenase, ferritin, serum iron, transferrin, transferrin saturation, transferrin soluble receptor, lymphocyte count and NK, CD3, CD4, CD8 and B subgroups of 31 patients during the first 2 weeks of their ICU stay. Correlation with mortality and severity at the time of admission was tested with the Spearman coefficient and Mann–Whitney test. Trends over time were tested with the Kruskal–Wallis analysis. Results: Lymphopenia is severe and constant, with a nadir on day 2 of ICU stay (median 0.555 109/L; interquartile range (IQR) 0.450 109/L); all lymphocytic subgroups are dramatically reduced in critically ill patients, while CD4/CD8 ratio remains normal. Neither ferritin nor lymphocyte count follows significant trends in ICU patients. Transferrin saturation is extremely reduced at ICU admission (median 9%; IQR 7%), then significantly increases at days 3 to 6 (median 33%, IQR 26.5%, p value 0.026). The same trend is observed with serum iron levels (median 25.5 μg/L, IQR 69 μg/L at admission; median 73 μg/L, IQR 56 μg/L on days 3 to 6) without reaching statistical significance. Hyperferritinemia is constant during intensive care stay: however, its dosage might be helpful in individuating patients developing haemophagocytic lymphohistiocytosis. D-dimer is elevated and progressively increases from admission (median 1319 μg/L; IQR 1285 μg/L) to days 3 to 6 (median 6820 μg/L; IQR 6619 μg/L), despite not reaching significant results. We describe trends of all the abovementioned parameters during ICU stay. Conclusions: The description of iron metabolism and lymphocyte count in Covid-19 patients admitted to the intensive care unit provided with this paper might allow a wider understanding of SARS-CoV-2 pathophysiology. [ABSTRACT FROM AUTHOR]

This paper investigates the probability of a radar being able to resolve two point-targets in an environment in which additive noise is the primary limitation on resolution. The statistical properties of the radar cross-ambiguity function are developed and used to arrive at an expression for the target resolution probability. The analysis is verified through simulations. [ABSTRACT FROM AUTHOR]

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias. [ABSTRACT FROM AUTHOR]

Introduction: Egypt’s National Road Project is a large infrastructure project which presently aims to upgrade 2500 kilometers of road networks as well as construct 4000 kilometers of new roads to meet today’s need. This leads to an increase in the number of work zones on highways and therefore a rise in hazardous traffic conditions. This is why highways agencies are shifting towards night construction in order to reduce the adverse traffic impacts on the public. Although many studies have investigated work zone crashes, only a few studies provide comparative analysis of the difference between nighttime and daytime work zone crashes. Methods: Data from Egyptian long-term highway work zone projects between 2010 and 2016 are studied with respect to the difference in injury severity between nighttime and daytime crashes by using separate mixed logit models. Results: The results indicate that significant differences exist between factors contributing to injury severity. Four variables are found significant only in the nighttime model and four other variables significant in the daytime model. The results show that older and male drivers, the number of lane closures, sidewise crashes, and rainy weather have opposite effects on injury severity in nighttime and daytime crashes. The findings presented in this paper could serve as an aid for transportation agencies in development of efficient measures to improve safety in work zones. [ABSTRACT FROM AUTHOR]

In this paper, we show that the cumulative and probability distribution functions (cdf and pdf, respectively) of small-scale wideband signal envelope fading in microcells can be very closely approximated by both the Rice and Nakagami distributions, which are already known to describe the cdfs and pdfs of small-scale narrowband signal envelope fading. Our results are obtained by means of an extensive Monte Carlo-based study of a wideband propagation model by Yan and Kozono, which is itself supported by extensive measurements. Over a comprehensive range of microcell propagation parameters, the average maximum error in the approximation is 1.65% and 1.78% for the Rice and Nakagami cdfs, respectively. The error in the approximation is 3.58% and 3.87% for the 95th percentile and does not exceed 7.61% and 7.80%, respectively, in the worst case. We propose an expression that maps the significant wideband small-scale signal envelope fading parameters to their narrowband counterparts for different standards like Dedicated Short Range Communication, WiMAX, Universal Mobile Telecommunications Service, and the family of the IEEE 802.11 standards. This mapping enables narrowband small-scale signal envelope fading statistical distributions, which are currently used, e.g., in fading simulators, link quality determination algorithms, and outage probability calculators, to be readily adapted to deal with small-scale wide-band signal envelope fading. As an application example of this proposed mapping, we derive the appropriate sample spacing and averaging interval (window size) that ought to be used to estimate the large-scale fading signal in an IEEE 802.11 receiver. [ABSTRACT FROM AUTHOR]

Multiuser diversity has been shown to increase the throughput of mobile ad hoc wireless networks (MANETs) when compared to fixed wireless networks. This paper addresses a multiuser diversity strategy that permits one of multiple one-time relays to deliver a packet to its destination. We show that the e (1) throughput of the original single one-time relay strategy is preserved by our multi-copy technique. The reason behind achieving the same asymptotic throughput is the fact that, as we demonstrate in this paper, interference for communicating among closest neighbors is bounded for different channel path losses, even when n goes to infinity. We show that a significant delay reduction is possible by multi-copy relaying when n is finite. Furthermore, we find that the average delay and delay variance for both the one and multi-copy relay strategies scale like Θ(m) and Θ(n²), respectively. We derive an approximation of the delay for multi-copy forwarding scheme and demonstrate that this approximation is very close to simulation results in MANET systems. [ABSTRACT FROM AUTHOR]

Variability in digital integrated circuits makes timing verification an extremely challenging task. In this paper, a canonical first-order delay model that takes into account both correlated and independent randomness is proposed. A novel linear-time block-based statistical timing algorithm is employed to propagate timing quantities like arrival times and required arrival times through the timing graph in this canonical form. At the end of the statistical timing, the sensitivity of all timing quantities to each of the sources of variation is available. Excessive sensitivities can then be targeted by manual or automatic optimization methods to improve the robustness of the design. This paper also reports the first incremental statistical timer in the literature, which is suitable for use in the inner loop of physical synthesis or other optimization programs. The third novel contribution of this paper is the computation of local and global criticality probabilities. For a very small cost in computer time, the probability of each edge or node of the timing graph being critical is computed. Numerical results are presented on industrial application-specified integrated circuit (ASIC) chips with over two million logic gates, and statistical timing results are compared to exhaustive corner analysis on a chip design whose hardware showed early mode timing violations. [ABSTRACT FROM AUTHOR]

Plotting order-ranked data is a standard technique that is used in estimating the probability of extreme weather events. Typically, observations, say, annual extremes of a period of N years, are ranked in order of magnitude and plotted on probability paper. Some statistical model is then fitted to the order-ranked data by which the return periods of specific extreme events are estimated. A key question in this method is as follows: What is the cumulative probability P that should be associated with the sample of rank m? This issue of the so-called plotting positions has been debated for almost a century, and a number of plotting rules and computational methods have been proposed. Here, it is shown that in estimating the return periods there is only one correct plotting position: P = m/(N + 1). This formula predicts much shorter return periods of extreme events than the other commonly used methods. Thus, many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated. [ABSTRACT FROM AUTHOR]

The cutoff rate R0(W) of a discrete memoryless channel (DMC) W is often used as a figure of merit alongside the channel capacity C(W). If a channel W is split into two possibly correlated subchannels W1 , W2, the capacity function always satisfies C(W1) + C(W2) ≤ C(W), while there are examples for which R0 (W1) + R0 (W2) > R0 (W). The fact that cutoff rate can be "created" by channel splitting was noticed by Massey in his study of an optical modulation system. This paper gives a general framework for achieving similar gains in the cutoff rate of arbitrary DMCs by methods of channel combining and splitting. The emphasis is on simple schemes that can be implemented in practice. We give several examples that achieve significant gains in cutoff rate at little extra system complexity. Theoretically, as the complexity grows without bound, the proposed framework is capable of boosting the cutoff rate of a channel to arbitrarily close to its capacity in a sense made precise in the paper. Apart from Massey's work, the methods studied here have elements in common with Forney's concatenated coding idea, a method by Pinsker for cutoff rate improvement, and certain coded-modulation techniques, namely, Ungerboeck's set-partitioning idea and Imai-Hirakawa multilevel coding; these connections are discussed in the paper. [ABSTRACT FROM AUTHOR]

Accelerated life tests are extensively used to provide quickly the information about the life distributions of products. Test units are subjected to elevated stresses which yield shorter lives. For some products whose life is defined by usage, e.g., mileage and cycles, test units are also run at higher usage rates (UR) to compress the test time. This paper presents a method for testing products at both higher stress levels, and UR. Censoring time is pre-determined and fixed, while censoring usage is a function of UR. A UR effect model is proposed to describe the dependence of usage to failure (UTF) on UR. The relationship between UTF, and stress and UR is established, and used to estimate the UTF distribution at design stresses and usual UR. The mode! parameters are estimated by maximum likelihood method. The best compromise test plans, which choose the UR, stress levels, and sample sizes, are devised by minimizing the asymptotic variance of the estimator of a life percentile at design stresses and usual UR. The efficiency, and sensitivity of the test plans are evaluated. The results show that the test plans are efficient, and robust. [ABSTRACT FROM AUTHOR]

In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome. [ABSTRACT FROM AUTHOR]

Background: Drawing on prospective data from the National Health and Nutrition Examination Survey (NHANES), our goal was to construct and validate a 5-year survival prediction model for individuals with cognitive impairment (CI). Methods: This study entailed a prospective cohort design utilizing information from the 2011-2014 NHANES dataset, encompassing individuals aged 40 years or older, with updated mortality status as of December 31, 2019. Predictive models within the derivation and validation cohorts were assessed using logistic proportional risk regression, column-line plots, and least absolute shrinkage and selection operator (LASSO) binomial regression models. Results: The study enrolled a total of 1,439 participants (677 men, mean age 69.75 ± 6.71 years), with the derivation and validation cohorts consisting of 1,007 (538 men) and 432 (239 men) individuals, respectively. The 5-year mortality rate stood at 16.12% (n = 232). We devised a 5-item column-line graphical model incorporating age, race, stroke, cardiovascular disease (CVD), and blood urea nitrogen (BUN). The model exhibited an area under the curve (AUC) of 0.772 with satisfactory calibration. Internal validation demonstrated that the column-line graph model displayed strong discrimination, yielding an AUC of 0.733, and exhibited good calibration. Conclusion: To sum up, our study successfully developed and internally validated a 5-item nomogram integrating age, race, stroke, cardiovascular disease, and blood urea nitrogen. This nomogram exhibited robust predictive performance for 5-year mortality in individuals with CI, offering a valuable tool for prognostic evaluation and personalized care planning. [ABSTRACT FROM AUTHOR]

Pasteurised donor human milk is recommended for very low birthweight infants who do not have access to their mother's milk. Although the use of donor milk continues to increase, little is known about the donation experiences of milk bank donors. We aimed to describe and compare enablers, barriers and patterns of human milk donation and identify factors predicting donation volume in a convenience sample of approved milk bank donors in the United Kingdom and the United States. A cross‐sectional online survey was conducted from August 2022 to December 2022. Approved milk bank donors (n = 556) from three milk banks in the United States (n = 369, Mothers' Milk Bank of Florida, Mother's Milk Bank of North Texas and Northwest Mothers Milk Bank) and one milk bank in the United Kingdom (n = 187, Hearts Milk Bank) completed the survey. A substantial portion of donors in both settings reported participating in other forms of milk exchange (51% of U.S. donors vs. 39% of UK donors, p = 0.009). Top donation barriers reported in both settings were completing the serological screening and having enough space to store collected milk. Most donors started donating when their infant was 3 months old or older and reported donating mature milk. The most common source of information related to milk banking in each setting was the internet (United Kingdom–70% vs. United States ‐ 63%, p = 0.112). Variables that predicted lifetime donation volume differed between the United States and the United Kingdom, highlighting the importance of setting‐specific milk banking research. Key messages: Milk bank donors in the United Kingdom and the United States reported primarily receiving milk banking information from the internet and not their health care provider.A substantial portion of human milk bank donors in the United Kingdom and the United States participate in other forms of milk exchange, including sharing with a peer.Participating in other forms of milk exchange did not predict lower milk bank donation volumes.Barriers to donation and predictors of donation volume differed between donors in the United Kingdom and the United States, suggesting setting‐specific research is warranted to improve the donation process globally. [ABSTRACT FROM AUTHOR]

Background: A negative attitude towards abortion among health care providers providing abortion services could be an obstacle even under a law, which permits abortion on request. Healthcare providers are expected to perform and be change agents of abortion services. However, little information is known about the attitude toward safe abortion among healthcare providers in Ethiopia. Objective: This study aimed to assess health care provider's attitudes towards safe abortion care and its associated factors at the public health facilities of Bahir Dar City, Northwest Ethiopia. Methods: A health facility-based cross-sectional study was employed from March 1 to 30/2021 among 416 health-care providers. The data were collected by computer-based generated simple random sampling technique, entered, coded, and cleaned using Epi data version 4.2 and analyzed using Statistical Package of Social Sciences version 25.0. Bivariate and multivariable logistic regression analyses were employed to estimate the crude and adjusted odds ratio with a confidence interval of 95% and a P-value of less than 0.05 considered statistically significant. Results: The response rate of the study was 99.3%, and 70.2% [95% CI: 65.6–74.6] of health-care providers had a favorable attitude towards safe abortion care. Multivariable analysis indicated that health care providers who are found in the age group of 25–29, 30–34, and ≥ 35 years [AOR = 3.34, 95% CI = 1.03–10.85], [AOR = 4.58, 95% CI = 1.33- 15.83] and [AOR = 5.30, 95% CI = 1.43–19.66] respectively, male health care providers [AOR = 3.20, 95% CI = 1.55–6.60], midwives [AOR = 6.50, 95% CI = 2.40–17.44], working at hospital [AOR = 4.77, 95% CI = 1.53–14.91], ever trained on safe abortion [AOR = 5.09, 95% CI = 2.29–11.32], practicing of an abortion procedure [AOR = 2.52, 95%, CI = 1.13–5.60], knowledge of abortion [AOR = 7.35, 95% CI = 3.23–16.71], awareness on revised abortion law [AOR = 6.44, 95% CI = 3.15–13.17] and need further legalization of abortion law [AOR = 11.78, 95% CI = 5.52–24.26] were associated with a favorable attitude towards safe abortion care. Conclusions: Healthcare providers who had a favorable attitude toward safe abortion care were relatively high compared to the previous studies. Age, sex, profession, workplace, training, knowledge, and practice-related factors were associated with a favorable attitude toward safe abortion. This study indicated that, a need for intervention to help improve the attitude of healthcare providers toward safe abortion care, especially for those working in the maternity care units. [ABSTRACT FROM AUTHOR]

Background: Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030. Methods: We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030. Results: The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential. Conclusion: Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning. [ABSTRACT FROM AUTHOR]

(1) Background: This study aimed to determine whether standard-dose vitamin D supplementation could provide adequate levels in exclusively breastfed infants born with different cord 25(OH)D levels and to evaluate related neonatal morbidities. (2) Methods: A prospective cross-sectional study was designed in term infants. Three groups were formed based on cord 25(OH)D levels; Group1 Deficiency:25(OH)D < 12 ng/mL, Group2 Insufficiency:25(OH)D = 12–19 ng/mL, and Group3 Optimum:25(OH)D = 20–100 ng/mL. Cord and 1st month 25(OH)D levels, after receiving standard-dose vitamin D prophylaxis, neonatal outcomes and anthropometric measurements were compared. (3) Results: The study involved 86 infants. Group1 Deficiency had also significantly lower 25(OH)D levels at 1st month compared to the others (p < 0.001). There was a significant positive correlation between cord and 1st month 25(OH)D levels (r = 0.78, p < 0.001). Despite the fact that the mother's age and clothing style were similar, Group1 Deficiency mothers had higher parity numbers and used less vitamin D during pregnancy (p = 0.03, p = 0.04). Neonatal outcomes analysis revealed that newborns in Group1 Deficiency experienced more respiratory distress, transient tachypnea, and early-onset sepsis, as well as more and longer hospital stays in the first-month of life (p < 0.05). (4) Conclusions: Infants with low cord 25(OH)D levels had also lower 25(OH)D values in the 1st month of life and experienced higher rates of neonatal morbidities. Given its numerous biological activities and impact on neonatal morbidities, determining an individualized dose of vitamin D supplementation may be more accurate than using the standard approach. [ABSTRACT FROM AUTHOR]

Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse linear mixed model, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals (n = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel (n = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel (n = 32,470 Europeans). These imputation errors had an impact on whether the CpG-SNP was included in the 95% credible set, with a difference of ~23% and ~7% between the WGS and the 1000G and HRC imputed datasets, respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong linkage disequilibrium with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low-level biological trait with imputed genetic data has implications for the study of higher-order complex traits and disease. [ABSTRACT FROM AUTHOR]

The reuse of business processes (BPs) requires similarities between them to be suitably identified. Various approaches have been introduced to address this problem, but many of them feature a high computational cost and a low level of automation. This paper presents a clustering algorithm that groups business processes retrieved from a multimodal search system (based on textual and structural information). The algorithm is based on Incremental Covering Arrays (ICAs) with different alphabets to determine the possible number of groups to be created for each row of the ICA. The proposed algorithm also incorporates Balanced Bayesian Information Criterion to determine the optimal number of groups and the best solution for each query. Experimental evaluation shows that the use of ICAs with strength four (4) and different alphabets reduces the number of solutions needed to be evaluated and optimizes the number of clusters. The proposed algorithm outperforms other algorithms in various measures (precision, recall, and F-measure) by between 12% and 88%. Friedman and Wilcoxon non-parametric tests gave a 90–95% significance level to the obtained results. Better options of repository search for BPs help companies to reuse them. By thus reusing BPs, managers and analysts can more easily get to know the evolution and trajectory of the company processes, a situation that could be expected to lead to improved managerial and commercial decision making. [ABSTRACT FROM AUTHOR]

Shooting skill in the NBA is typically measured by field goal percentage (FG%) – the number of makes out of the total number of shots. Even more advanced metrics like true shooting percentage are calculated by counting each player's 2-point, 3-point, and free throw makes and misses, ignoring the spatiotemporal data now available (Kubatko et al. 2007). In this paper we aim to better characterize player shooting skill by introducing a new estimator based on post-shot release shot-make probabilities. Via the Rao-Blackwell theorem, we propose a shot-make probability model that conditions probability estimates on shot trajectory information, thereby reducing the variance of the new estimator relative to standard FG%. We obtain shooting information by using optical tracking data to estimate three factors for each shot: entry angle, shot depth, and left-right accuracy. Next we use these factors to model shot-make probabilities for all shots in the 2014–2015 season, and use these probabilities to produce a Rao-Blackwellized FG% estimator (RB-FG%) for each player. We demonstrate that RB-FG% is better than raw FG% at predicting 3-point shooting and true-shooting percentages. Overall, we find that conditioning shot-make probabilities on spatial trajectory information stabilizes inference of FG%, creating the potential to estimate shooting statistics earlier in a season than was previously possible. [ABSTRACT FROM AUTHOR]

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron’s procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers. [ABSTRACT FROM AUTHOR]

Quigley derived his hybrid price model to improve the precision of predicted prices of sold homes by statistically merging data of resold homes in a repeat sales model with that of once-sold homes in a single sales hedonic price model. The literature has few applications of the hybrid model aside from those by Quigley and his collaborators. Two reasons for this underuse may be its computational intensiveness and its marginal empirical improvement in comparison with two other models. This paper first demystifies this computational intensiveness by calibrating models in Microsoft Excel with transferable procedures into other software. It second evaluates the hybrid price model’s empirical improvement as a reason for its underuse by predicting prices of 2,559 sold and resold homes observed in two inner-city neighbourhoods in Windsor, Ontario, during a 30-year period. The results as hypothesized are its lower standard errors of regression coefficients and higher simple R-squared than those of a single sales hedonic price model. Moreover, the hybrid model’s predictions have higher correlations than those of the single sales model with not only in-sample observed prices or changes in prices but also out-of-sample ones. The conclusion speculates in plans for future research about reasons for two models’ similar or dissimilar regression coefficients and standard errors predicting correspondingly similar or dissimilar sale prices of homes through time. [ABSTRACT FROM AUTHOR]

This paper develops the ability of the normal inverse Gaussian distribution (NIG) to fit the returns of bitcoin (BTC). As the first cryptocurrency created, the behavior of this new asset is characterized by great volatility. The lack of a proper definition or classification under existing theory exacerbates this property in such a way that explosive periods followed by a rapid decline have been observed along the series, meaning bubble episodes. By detecting the periods in which a bubble rises and collapses, it is possible to study the statistical properties of such segments. In particular, adjusting a theoretical distribution may help to determine better strategies to hedge against these episodes. The NIG is an appropriate candidate not only because of its heavy-tailed property but also because it has been proven to be closed under convolution, a characteristic that can be implemented to measure multivariate value at risk. Using data on the price of BTC with respect to seven of the main global currencies, the NIG was able to fit every time segment despite the bubble behavior. In the out-of-sample tests, the NIG was proven to have an adjustment similar to that of a generalized hyperbolic (GH) distribution. This result could serve as a starting point for future studies regarding the statistical properties of cryptocurrencies as well as their multivariate distributions. [ABSTRACT FROM AUTHOR]

The understanding of human mobility patterns in different transportation modes is an interdisciplinary research field with a direct impact in aspects as varied as urban planning, traffic optimization, sustainability, the reduction of operating costs as well as the mitigation of pollution in urban areas. In this paper, we study the global activity of users in bike-sharing systems operating in the cities of Chicago and New York. For this transportation mode, we explore the temporal and spatial characteristics of the mobility of cyclists. In particular, through the analysis of origin-destination matrices, we characterize the spatial structure of the displacements of users. We apply a mobility model for the global activity of the system that classifies the displacements between stations in local and non-local transitions. In local transitions, cyclists move in a region around each station whereas, in the non-local case, bike users travel with long-range displacements in a similar way to Lévy flights. We reproduce the spatial dynamics by using Monte Carlo simulations. The obtained results are similar to the observed in real data and reveal that the model implemented captures important characteristics of the global spatial dynamics in the systems analyzed. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure. The modeling includes covariates of the demand, which are used as predictors of this. We describe an algorithm that summarizes the methodology and we discuss its computational framework. A case study with unpublished real-world data is presented to illustrate the potential of this methodology. We report that the accuracy of the demand variance estimator improves when a temporal structure is considered, instead of assuming time-independent demand. The methodology is useful in decisions related to inventory logistics management when the demand shows patterns of temporal dependence. [ABSTRACT FROM AUTHOR]

Objectives: Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context. Multicollinearity in particular, if not treated appropriately, can be problematic and might lead to erroneous conclusions. In this paper we present a novel ‘leave one variable out’ (LOVO) partial least squares correlation analysis (PLSCA) methodology, designed to overcome the problem of multicollinearity, and show how this can be used to identify the training load (TL) variables that influence most ‘end fitness’ in young rugby league players. Methods: The accumulated TL of sixteen male professional youth rugby league players (17.7 ± 0.9 years) was quantified via GPS, a micro-electrical-mechanical-system (MEMS), and players’ session-rating-of-perceived-exertion (sRPE) over a 6-week pre-season training period. Immediately prior to and following this training period, participants undertook a 30–15 intermittent fitness test (30-15IFT), which was used to determine a players ‘starting fitness’ and ‘end fitness’. In total twelve TL variables were collected, and these along with ‘starting fitness’ as a covariate were regressed against ‘end fitness’. However, considerable multicollinearity in the data (VIF >1000 for nine variables) meant that the multiple linear regression (MLR) process was unstable and so we developed a novel LOVO PLSCA adaptation to quantify the relative importance of the predictor variables and thus minimise multicollinearity issues. As such, the LOVO PLSCA was used as a tool to inform and refine the MLR process. Results: The LOVO PLSCA identified the distance accumulated at very-high speed (>7 m·s-1) as being the most important TL variable to influence improvement in player fitness, with this variable causing the largest decrease in singular value inertia (5.93). When included in a refined linear regression model, this variable, along with ‘starting fitness’ as a covariate, explained 73% of the variance in v30-15IFT ‘end fitness’ (p<0.001) and eliminated completely any multicollinearity issues. Conclusions: The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between ‘end fitness’ and the predictor variable ‘accumulated distance at very-high speed’ when ‘starting fitness’ was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies. [ABSTRACT FROM AUTHOR]

Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system. [ABSTRACT FROM AUTHOR]

Quantitative applied linguistics research often takes place in restricted settings of an intact language classroom, workplace, phonetics laboratory or longitudinal sample. In such settings the samples tend to be small, which raises several methodological problems. The main aim of the current paper is to give a detailed explanation of methodological and practical implications inherent in a robust statistical method called bootstrapped quantile regression (BQR) analysis. Importantly for applied linguistics research, the BQR method could help to deal with methodological difficulties inherent in small sample studies. The current study employed a moderately small sample (N = 27) of students learning the Japanese language in a Malaysian public university. It examined the relationships between the students’ language learning motivation (specifically, integrative orientation), the students’ images or stereotypes about Japan and their global attitudes toward the target language country and its people. The findings indicated that there was a statistically significant relationship between the students’ attitudes toward the target language country and their integrative orientation. In addition, these attitudes were found to be the most constant determinant of the integrative orientation. Besides the applied linguistics research, the BQR method can be used in a variety of the human sciences research where a sample size is small. [ABSTRACT FROM AUTHOR]

This paper studies the performance of a cell-averaging constant false alarm rate (CA-CFAR) receiver that operates in a nonhomogeneous clutter environment with impulsive, heavy-tailed statistics. The clutter distribution is modeled by a positive alpha-stable distribution, which is described by the characteristic exponent ?, where 0 < α <1. Analytical expressions for the false alarm and detection probabilities are derived for arbitrary values of α assuming a nonhomogeneous clutter background. The analytical results are validated with computer simulation. [ABSTRACT FROM PUBLISHER]

INTRODUCTION: The Medicines (Standing Order) Amendment Regulations 2011 allow medicines to be supplied or administered to a patient by a nurse in the absence of a medical practitioner and without a prescription. Regulations have been in place since 2002, but no substantive research has occurred in New Zealand concerning their use. AIM: This paper reports a survey of registered nurses (RNs) who work in primary health care (PHC) settings and explores aspects of their practice relating to their use of standing orders. METHODS: A self-reported survey using a non-probability sample of RNs working in PHC who use standing orders in their practice (n=231). Data were analysed descriptively. RESULTS: The sample were experienced RNs (mean 24 years since registration) and 53% have a post-graduate qualification. Some nurses' understanding of a standing order included provision of a prescription to a patient. Standing orders were used frequently (42% reported use 1 to >5 times/day) for a wide variety of conditions. There is a significant relationship between undertaking the stated professional development requirements and confidence in the clinical decisions made (p=0.025). Over half (52%) would like to use standing orders more often. DISCUSSION: Standing orders are used extensively in PHC settings. The conditions nurses are involved in treating are usually already differentiated or have a high degree of diagnostic certainty. Nurses can effectively provide medicines under standing orders when doctors support their use, issue evidence-based orders, and have confidence in nurses with advanced skills. Doctors need to meet their responsibilities under the Regulations. [ABSTRACT FROM AUTHOR]