Search

Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in empirically relevant settings there need not exist a posterior draw for the impulse response function that matches the posterior median or mean response function, even as the number of posterior draws approaches infinity. As a result, the use of these summary statistics may distort the shape of the impulse response function which is of foremost interest in applied work. The same concern applies to error bands based on the upper and lower quantiles of the marginal posterior distributions of the impulse responses. In addition, these error bands fail to capture the full uncertainty about the estimates of the structural impulse responses. In response to these concerns, we propose new estimators of impulse response functions under quadratic loss, under absolute loss and under Dirac delta loss that are consistent with Bayesian statistical decision theory, that are optimal in the relevant sense, that respect the dynamics of the impulse response functions and that are easy to implement. We also propose joint credible sets for these estimators derived under the same loss function. Our analysis covers a much wider range of structural VAR models than previous proposals in the literature including models that combine short-run and long-run exclusion restrictions and models that combine zero restrictions, sign restrictions and narrative restrictions.

Mobile Cloud Computing enables the migration of services to the edge of the Internet. Therefore, high-performance computing clusters are widely deployed to improve computational capabilities of such environments. However, they are prone to failures and need analytical models to predict their behaviour in order to deliver desired quality-of-service and quality-of-experience to mobile users. This paper proposes a 3D analytical model and a problem-solving approach for sustainability evaluation of high-performance computing clusters. The proposed solution uses an iterative approach to obtain performance measurements to overcome the state space explosion problem. The availability modelling and evaluation of master and computing nodes are performed using a multi-repairman approach. The optimum number of repairmen is also obtained to get realistic results and reduce the overall cost. The proposed model is validated using discrete event simulation. The analytical approach is much faster and in good agreement with the simulations. The analysis focuses on mean queue length, throughput, and mean response time outputs. The maximum differences between analytical and simulation results in the considered scenarios of up to a billion states are less than1.149%,3.82%, and3.76%respectively. These differences are well within the5%of confidence interval of the simulation and the proposed model.

We consider a general electric vehicle (EV) charging system with stochastic demand, demand request locations, and predetermined charging facilities (including charging station locations and charger capacities). The objective is to design a good routing strategy that accommodates well demand-request dynamics so as to satisfy the charging system’s stability constraints and also minimize the EV’s mean response time. We introduce a class of flexible and measurement-based routing policies called “partition-based random routing” (PBRR) and show that the performance measure of interest can be formulated as a constrained optimization problem with a convex objective function when the system is heavily loaded. This formulation enables us to establish strong theoretical results that are in aid of finding the optimal routing solution; however, in practice, finding this solution requires rather involved numerical calculations. To that end, we propose a surrogate, easy to design and implement, optimization algorithm for finding the desired optimal routing solution. Numerical work based on synthetic data shows that the performance of the developed routing strategy and its fast implementation is highly satisfactory for a number of system settings.

A process variability analysis is carried out to find the most influencing input parameter and corresponding output parameter variation using the L9 approach. The common factors, levels, parameters, and responses are mentioned in the following section. The mean response curve for the output parameters is also presented. Empirical relations are obtained for finding the responses in terms of the common factors. EBX936 (Non-Crimp bidirectional, Glass Reinforcement Manufactured by-Tech. Textile, USA) with 3, 6, 12 number of layer (NOL) is the material system used in the analysis. The infusion pressure is kept in the workable range of 60 to 70 kPa as normally used in the VARTM process. Very high vacuum at inlet increases the resin velocity through the preform and hence the time of infusion will reduce drastically, giving very little time for proper saturation of fibers in the preform. Therefore, to keep the velocity in the middle range of velocities during the infusion low injection pressures are selected. The viscosity of the resin is selected in the range 0.2 to 0.3 Pa.s.

Objective: Identify key demographic factors and modes of follow-up in surgical survey response. Summary background data: Surveys are widely used in surgery to assess patient and procedural outcomes, but response rates vary widely which compromises study quality. Currently there is no consensus as to what the average response rate is and which factors are associated with higher response rates. Methods: The National Library of Medicine (MEDLINE/PubMed) was systematically searched from Januray 1, 2007 until February 1, 2020 using the following strategy: (((questionnaire) OR survey) AND "response rate") AND (surgery OR surgical). Original survey studies from surgical(-related) fields reporting on response rate were included. Through one-way analysis of variance we present mean response rate per survey mode over time, number of additional contacts, country of origin, and type of interviewee. Results: The average response is 70% over 811 studies in patients and 53% over 1746 doctor surveys. In-person surveys yield an average 76% response rate, followed by postal (65%) and online (46% web-based vs 51% email) surveys. Patients respond significantly more often than doctors to surveys by mail (P < 0.001), email (P = 0.003), web-based surveys (P < 0.001) and mixed mode surveys (P = 0.006). Additional contacts significantly improve response rate in email (P = 0.26) and web-based (P = 0.041) surveys in doctors. A wide variation in response rates was identified between countries. Conclusions: Every survey is unique, but the main commonality between studies is response rate. Response rates appear to be highly dependent on type of survey, follow-up, geography, and interviewee type.

You saw that the expectation is that the response variable is a continuous variable that is normally distributed. If the response variable is a discrete variable, you use a different regression method. If the response variable can take values such as yes/no or multiple discrete variables (for example, views such as strongly agree, agree, partially agree, and do not agree), you use logistic regression. You will explore logistic regression in a separate chapter.

NEW FINDINGS What is the central question of this study? During exercise, there are fluctuations in conduit artery blood flow (BF) caused by both cardiac and muscle contraction-relaxation cycles. What is the optimal method to process Doppler ultrasound-measured BF for the purpose of characterizing the dynamic response of BF during step-transitions in exercise? What is the main finding and its importance? Continuous BF data were processed in relation to either cardiac or muscle contraction-relaxation cycles and computed based on 'binned' or 'rolling' averages over one, two or five consecutive cycles. Kinetics characterization revealed no data processing technique-specific differences in steady-state BF, but variability in the rapidity at which BF attained steady-state (i.e., mean response time) was observed. ABSTRACT The overall rate of blood flow (BF) adjustment (i.e., kinetics) from the onset of an exercise transition can be quantified by the mean response time (MRT). However, the BF response profile can be distorted during rhythmic, dynamic exercise consequent to variations caused by the cardiac cycle (HR) and the muscle contraction-relaxation (CR) cycle. We examined the extent to which distortions imposed by HR and CR cycles affected BF kinetics. Eight healthy, young men (27 (4) years; mean (SD)) performed transitions of alternate-leg knee-extension exercise from 3 W to either a moderate- (MOD) or heavy-intensity (HVY) power output. Femoral artery BF was continuously measured by Doppler ultrasound and averaged over one, two or five 'binned' (e.g., HR2b, etc.) or 'rolling' (e.g., CR5r, etc.) HR and CR cycles. Among analysis techniques, there were no differences for steady-state BF values at the 3 W baseline. In MOD, MRT using contraction-relaxation cycle (CR1) was smaller than most other analysis techniques. For both MOD and HVY, the 95% confidence interval for MRT was generally larger when using HR- compared to CR-related methods, and monoexponential fits based on 'rolling' averages (HR2r, HR5r, CR2r, CR5r) had a poorer ability to estimate the true end-exercise BF in HVY than in MOD. When modelling BF kinetics, we conclude that the CR1 method is a good option because of its ability to accurately estimate the 'data-determined' end-exercise BF value from the 'model-derived' response, maintain a relatively high density of data points during the transition and yield a relatively small 95% CI.

The size and number of cloud data centers (CDCs) have grown rapidly with the increasing popularity of cloud computing and high-performance computing. This has the unintended consequences of creating new challenges due to inefficient use of resources and high energy consumption. Hence, this necessitates the need to maximize resource utilization and ensure energy efficiency in CDCs. One viable approach to achieve energy efficiency and resource utilization in CDC is task scheduling. While several task scheduling approaches have been proposed in the literature, there appears to be a lack of classification-based merging concept for real-time tasks in these existing approaches. Thus, an energy-efficient dynamic scheduling scheme (EDS) of real-time tasks for virtualized CDC is presented in this paper. In the scheduling scheme, the heterogeneous tasks and virtual machines are first classified based on a historical scheduling record. Then, similar type of tasks are merged and scheduled to maximally utilize an operational state of the host. In addition, energy efficiencies and optimal operating frequencies of heterogeneous physical hosts are employed to attain energy preservation while creating and deleting the virtual machines. Experimental results show that, in comparison with existing techniques, EDS significantly improves overall scheduling performance, achieves a higher CDC resource utilization, increases task guarantee ratio, minimizes the mean response time, and reduces energy consumption.

We propose a nonparametric bivariate varying coefficient generalized linear model to predict a mean response trajectory in the future given an individual's characteristics at present or an earlier time point in a longitudinal study. Given the measurement time of the predictors, the coefficients vary as functions of the future time over which the prediction of the mean response is concerned and illustrate the dynamic association between the future response and the earlier measured predictors. We use a nonparametric approach that takes advantage of features of both the kernel and the spline methods for estimation. The resulting coefficient estimator is asymptotically consistent under mild regularity conditions. We also develop a new bootstrap approach to construct simultaneous confidence bands for statistical inference about the coefficients and the predicted response trajectory based on the coverage rate of bootstrap estimates. We use the Framingham Heart Study to illustrate the methodology. The proposed procedure is applied to predict the probability trajectory of hypertension risk given individuals' health condition in early adulthood and to examine the impact of risk factors in early adulthood on a long-term risk of hypertension over several decades.

Clustered binary samples arise often in biomedical investigations. An important feature of such samples is that the binary responses within clusters tend to be correlated. The Beta-Binomial model is commonly ap- plied to account for the intra-cluster correlation - the correlation between responses within the clusters - among dichotomous outcomes in cluster sam- pling. The intracluster correlation coefficient (ICC) quantifies this correla- tion or level of similarity. In this paper, we propose Bayesian point and inter- val estimators for the ICC under the Beta-Binomial model. Using Laplace's method, the asymptotic posterior distribution of the ICC is approximated by a normal distribution. The posterior mean of this normal density is used as a central point estimator for the ICC, and 95% credible sets are calculated. A Monte Carlo simulation is used to evaluate the coverage probability and average length of the credible set of the proposed interval estimator. The simulations indicate that for the situation when the number of clusters is above 40, the underlying mean response probability falls in the range of (0.3;0.7), and the underlying ICC values are • 0.4, the proposed interval estimator performs quite well and attains the correct coverage level. Even for number of clusters as small as 20, the proposed interval estimator may still be useful in the case of small ICC (• 0.2).

The study seeks to ascertain Nigerian language teachers’ awareness of pedagogical gestures as an enhancement to teaching and whether this awareness is influenced by teaching location and teacher qualification. The study adopts the survey research design with a sample size of 262 drawn using multi-staged sampling techniques. The Z-test method was used in testing the hypothesis of the study. It was discovered that there was high mean response of teachers on the awareness of pedagogical gestures. There were significant differences in the mean response of urban/rural teachers and no significant differences in the mean response of graduate/non-graduate teacher’s awareness of pedagogical gestures as enhancement technique. Our conclusion is that pedagogical gestures are indispensable in lesson delivery and that their pedagogic values are gained through awareness of them as enhancement technique.

The purpose of this study was to analyze the effectiveness of the implementation of training in the working area (PDWK) of ICT for MTs teachers in the district. Bojonegoro. This type of research is a pre-experimental research design with one group preset-postest design. Data on student activities were analyzed descriptively, data on participant learning outcomes were analyzed using paired t-test and data on participants' responses to training were analyzed descriptively. The results showed: (1) The participants' activity with an average observation result was 79.14% with very high criteria; (2) the criteria for improving student learning outcomes obtained a p-value of 0.000

Random-effects models are frequently used to analyze clustered binomial data. The direct computation of the marginal mean response, when integrated over the distribution of random effects, is challenging due to taking nonclosed-form expressions of the marginal link function. This paper extends the marginalized modeling methodology using innovative link functions, where the marginal mean response is modeled in terms of covariates and random effects. To derive the explicit closed-form representation of both marginal and conditional means, the regression structure is designed through an original strategy to introduce particular random-effects distributions. It will consequently allow for a reasonable interpretation of covariate effects. A Bayesian approach is employed to make the statistical inference by implementing the Markov chain Monte Carlo scheme. We conducted simulation studies to show the usefulness of our methodology. Two real-life data sets, taken from the teratology and respiratory studies, have been analyzed for illustration. The findings confirm that our new modeling methodology offers convenient settings for analyzing binomial responses in practice.

Several exposure limits for perchlorate have been developed based on an early key event, inhibition of radioactive iodide uptake (RAIU) by the thyroid. These assessments have used a variety of definitions of the point of departure. The current assessment revisited the modeling for inhibition of RAIU, using state of the science methods. Bayesian hierarchical modeling was used to account for the repeated measures on the same individuals in the key dataset, and the underlying Beta distribution used for the modeling correctly reflected the bounding of RAIU between 0 and 1. We defined the BMR as a point value of 8% RAIU (rather than a change in RAIU), based on descriptions in the medical literature that RAIU below this value is considered abnormal. Because a definition of the BMR based on the mean response would correspond to about 50% of the population with a response below the BMR at the benchmark dose, we used a hybrid definition of the BMR. That is, the BMD was defined as the dose at which it was estimated that there would be a 10% extra risk in the population of having RAIU of 8% or lower. The resulting point of departure based on the BMDL was 0.03 mg/kg-day.

Rathouz and Gao [2] and Luo and Tsai [3] proposed valuable extensions to the generalized linear model for modeling a nonlinear monotonic relationship between the mean response and a set of covariates. In their extensions for discrete data the baseline response distribution is unspecified and is estimated from the data. We propose to extend this model for the analysis of longitudinal data by incorporating random effects into the linear predictor, and using maximum likelihood for estimation and inference. Motivated in particular by longitudinal studies of clinical scale outcomes, we developed an estimation procedure for a finite-support response using a generalized expectation-maximization algorithm where Gauss-Hermite quadrature is employed to approximate the integrals in the E step of the algorithm. Upon convergence, the observed information matrix is estimated through second-order numerical differentiation of the log-likelihood function. Asymptotic properties of the maximum likelihood estimates follow under the usual regularity conditions. Simulation studies are conducted to assess its finite-sample properties and compare the proposed model to the generalized linear mixed model. The proposed method is illustrated in an analysis of data from a longitudinal study of Huntington's disease.

The effects of combinations of different types of irregularities have not been studied in details in the past and current seismic design codes do not address this issue appropriately. In this research, 76 regular and irregular bridges with irregularities in both superstructure and substructure were designed and evaluated to investigate the impact of combinations of irregularities on the seismic ductility demands. The irregularity parameters considered in this study include irregularities in span arrangement, different lengths of columns, different abutments support conditions and different stiffness of superstructure. The bridges were designed and checked according to AASHTO provisions. Inelastic time history analysis was conducted using OpenSees software and ductility demands in bridge columns for different bridge configurations were predicted. Predictions of ductility demands were based on the mean responses obtained using a number of ground motion records. Finally, the effect of considering displacement directions in predicting the mean bridge response (i.e., using different methods for predicting the mean response) for irregular and regular bridges was investigated. The results indicate that the combinations of irregularities can significantly increase the ductility demands in some cases compared to the case of regular bridges.

Relative potency is widely used in toxicological and pharmacological studies to characterize potency of chemicals. The relative potency of a test chemical compared to a standard chemical is defined as the ratio of equally effective doses (standard divided by test). This classical concept relies on the assumption that the two chemicals are toxicologically similar-that is, they have parallel dose-response curves on log-dose scale-and thus have constant relative potency. Nevertheless, investigators are often faced with situations where the similarity assumption is deemed unreasonable, and hence the classical idea of constant relative potency fails to hold; in such cases, simply reporting a single constant value for relative potency can produce misleading conclusions. Relative potency functions, describing relative potency as a function of the mean response (or other quantities), is seen as a useful tool for handling nonconstant relative potency in the absence of similarity. Often, investigators are interested in assessing nonconstant relative potency at a finite set of some specific response levels for various regulatory concerns, rather than the entire relative potency function; this simultaneous assessment gives rise to multiplicity, which calls for efficient statistical inference procedures with multiplicity adjusted methods. In this paper, we discuss the estimation of relative potency at multiple response levels using the relative potency function, under the log-logistic dose-response model. We further propose and evaluate three approaches to calculating multiplicity-adjusted confidence limits as statistical inference procedures for assessing nonconstant relative potency. Monte Carlo simulations are conducted to evaluate the characteristics of the simultaneous limits.

We study robust designs for generalized linear mixed models (GLMMs) with protections against possible departures from underlying model assumptions. Among various types of model departures, an imprecision in the assumed linear predictor or the link function has a great impact on predicting the conditional mean response function in a GLMM. We develop methods for constructing adaptive sequential designs when the fitted mean response or the link function is possibly of an incorrect parametric form. We adopt the maximum likelihood method for estimating the parameters in GLMMs and investigate both I-optimal and D-optimal design criteria for the construction of robust sequential designs. To study the empirical properties of these sequential designs, we ran a series of simulations using both logistic and Poisson mixed models. As indicated in the simulation results, the I-optimal design generally outperforms the D-optimal design for all scenarios considered. Both designs are more efficient than the conventionally used uniform design and the classical D-optimal design obtained under the assumption that the fitted models are correctly specified. The proposed designs are also illustrated in an example using actual data from a dose–response experiment.

In this study, to enhance the distillate production, an attempt was taken to fabricate a robust design single basin solar still. From earlier research findings, four operational parameters were identified as performance enhancing and they were – basin glass cover thickness, basin water depth, wick material used to wrap the fins and size of heat storage material (blue metal). The next task was to identify the optimum parameter level for each parameter. The 4 parameters and 3 parameter levels were combined as per L9 orthogonal array and the experimental results were analyzed using Taguchi method. The optimum parameter levels were identified using S/N ratio and mean response. The significant parameters and contribution to production were calculated using ANOVA technique. The nature and degree of relationship between independent variables and dependent variable was analyzed using regression technique. The optimum production was estimated and it was compared with theoretical production and actual experimental production. The combination of 4 mm thick glass basin cover, 10 mm basin water depth, fins wrapped with glass wool and 15 mm size heat storage material were identified as optimum combination of parameter levels. Wick materials around the fins and basin water depth played significant role in determining the production. The actual production from solar still designed as per Taguchi method was about 86.82% of estimated optimum production.

Background: Waiting for results is often associated with a state of anxiety and restlessness. The current study is intended to study the emotional state of people waiting for their results after undergoing COVID-19 testing. Methods: A descriptive study was done among 140 participants enrolled by simple random technique over a period of 1 month in May 2020. The participants who gave sample for COVID-19 were assessed for their emotional state at two stages – after giving sample and during the waiting period before telling results to them. A self-designed questionnaire focusing on the demographics and emotional state was submitted by the participants distributed to them as Google Forms. Wilcoxon signed-rank test and McNemar test were used to compare the statistical change in emotions over a period of time. Results: Around 63.6% and 68.8% of the participants had neutral emotional state after giving sample and before announcement of results, respectively. The mean score of agreement with decision to test was 9 ± 1.6 and discomfort related to testing was 4.6 ± 2.6 on Likert scale of 1–10. A statistically significant difference (P = 0.01) was observed in mean response for “something can be done to help” after giving sample (1.6 ± 0.4) and before announcement of results (1.8 ± 0.3). Conclusions: Patients undergoing testing for COVID-19 undergo psychological turmoil and use various coping methods. Patients endorsed various suggestions including quicker generation of results, environmental changes and counselling services to help them cope better. Incorporating these suggestions may improve the diagnostic process in patients with COVID-19.

Purpose – Pricing is a strategic competitive leverage and firms increasingly utilize data-driven pricing methods. Estimates of product-level price elasticities are needed to determine the best prices for each product, hence reliable estimation is of first-order importance. However, due to the increasing number of products and dynamics of customer behavior, relevant historical data is often limited. Design/methodology/approach – The objective of this paper is to jointly cluster products with similar price elasticities and estimate this cluster-specific quantity using regression clustering. An extension of the regression clustering problem. Two heuristics are proposed: The gradient descentbased heuristic iterates through feasible solutions to increase cluster-specific regression fit. The categorical ordering heuristic fits a regression for each product, orders the products based on the mean response, and splits them at the largest gap. Using simulated and real-world datasets, a comparative performance analysis is conducted. Findings – Using the gradient descent-based heuristic with multiple starting solutions gives the best performance. The computational times could decrease with smart initial solutions, which is especially critical if the number of products is large. The categorical ordering heuristic, the fastest method, performs better when there are more than two clusters but suffers from robustness problems. Discussion – The findings show that offered heuristics are effective to estimate product-specific price elasticity with limited data. Firms could leverage these estimates to increase revenues and profits by better aligning product prices with demand. Given that many products have limited relevant data, the extent of the applications of our method is quite large which, in turn, could help firms stay competitive.

A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in ( Nolan and Wand (2020) , ANZIAM Journal, doi: 10.1017/S1446181120000061). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.

The purpose of this study was to systematically review the methodology, response rate and quality of survey studies related to femoroacetabular impingement (FAI) syndrome. A search was conducted on three databases (PubMed, EMBASE, MEDLINE) for relevant studies from database inception to 27 January 2020. Data extracted included study and survey characteristics, as well as response rates. The quality of the included studies was also assessed using a previously published quality assessment tool. Data were analysed with means, ranges, standard deviations, 95% confidence intervals and bivariate analysis. Eleven studies (13 surveys) were included in this review out of a total of 1608 initial titles found. Surveys were most often administered via the Internet (72%) to orthopaedic surgeons (54%). The mean response rate was 70.4%. The mean quality score was moderate 13.3/24 (SD ±4.3). The criterion that most often scored high was ‘clearly defined purpose and objectives’ (11/11). The most common survey topic investigated surgeons’ knowledge regarding FAI diagnosis and management (n = 7). In addition, bivariate analysis between quality score and response rate showed no significant correlation (Spearman’s rho = −0.090, P = 0.85). Overall, survey studies related to FAI syndrome most often use Internet-based methods to administer surveys. The most common target audience is orthopaedic surgeons. The topics of the surveys most often revolve around orthopaedic surgeons’ knowledge and opinions relating to the diagnosis and management of FAI syndrome. The response rate is high in patient surveys and lower in larger surgeon surveys. Overall, the studies are of moderate quality.

Data with spatial dependence are often modeled by geoestatistical tools. In spatial regression, the mean response is described using explanatory variables with georeferenced data. This modeling frequently considers Gaussianity assuming the response follows a symmetric distribution. However, when this assumption is not satisfied, it is useful to suppose distributions with the same asymmetric behavior of the data. This is the case of the Birnbaum–Saunders (BS) distribution, which has been considered in different areas and particularly in environmental sciences due to its theoretical arguments. We propose a geostatistical model based on a new approach to quantile regression considering the BS distribution. Global and local diagnostic analytics are derived for this model. The estimation of model parameters and its local influence are conducted by the maximum likelihood method. Global influence is based on the Cook distance and it is compared to local influence, in both cases to detect influential observations, whose detection and removal can modify the conclusions of a study. We illustrate the proposed methodology applying it to environmental data, which shows this situation changing the conclusions after removing potentially influential observations. A comparison with Gaussian spatial regression is conducted.

Most of the braille embossers incorporate a mode for the creation of braille embossed graphics (BEG). Some of them can produce dots in different elevations, but also various dot densities. Dot elevation and dot density constitute the main variables that dominate the identification and conceptualization by blind users of tactile elements, such as lines and areas, in BEG. Nevertheless, there are not enough published research data in this domain. We present the results of a study with 30 congenitally blind participants to examine their detection performance in tactile identification of context-free embossed lines and square areas in eight dot elevations H1 to H8 (from 0.03 to 0.47 mm) and two dot densities (10 and 20 dpi). The detection accuracy and response time of the matching task were examined for each type of test stimuli. The results of correct and misclassified stimuli are presented in confusion matrixes, along with the overall mean response time for each task. The participants classify with better accuracy the stimuli with the three lower dot elevations (between 0.03 mm and 0.15 mm) for both types of stimuli and in both dot densities. The minimum overall mean response time of the identification task was achieved for the larger dot height H8 in the case of raised-dot square areas of 10 dpi. The analysis of the results indicates that better classification accuracy is achieved with the combination of four dot heights H1, H2, H4, and H8. Blind users can distinguish textures produced by dots with very low heights in line with the recent literature suggestions. The current study constitutes a first step toward recommendations for the designers of BEG in applying textures with variable dot height and dot density for educational purposes or for producing tactile maps to benefit users who are blind.

This paper considers a situation in which cause–effect relationships among variables can be described by a linear structural equation model (linear SEM) and the corresponding directed acyclic graph (DAG). By considering a set of covariates that satisfies the back-door criterion, we formulate (1) the variances of the estimated mean response and (2) the mean squared error (MSE) of the predicted response, with external intervention in which a treatment variable is set to be a certain constant value. The variance and MSE formulas proposed in this paper are exact, unlike those in most previous studies regarding the problem of estimating total effects. In addition, we compare the performance of the simple regression model with that of the predicted response with the external intervention. Furthermore, we apply the present results to statistical quality control.

—Consideration is given to the dispatching problem in an almost nonobservable distributed processing system with M, M ≥ 2, single-server queues operating in parallel, each under the processor-sharing discipline. Jobs having the same job size distribution arrive one-by-one to the dispatcher, which immediately routes it to one of the queues. When making a routing decision, the dispatcher has no online information about the system (like current queues sizes, size of arriving job, etc.). The only information available to the dispatcher is job-size distribution, job-interarrival-time distribution, server speeds, time instants of previously arrived jobs, and previous routing decisions. Under these conditions, one is interested in the routing policies that minimize the job long-run mean response time. A new class of dispatching policies is proposed that, according to the numerical experiments, may significantly outperform all classical dispatching policies available for such system: (optimal) probabilistic policy and the round-robin policy.

Torrefaction is a promising technology to improve fuel properties of biomass. However, torrefied char does not show densified energy content compared with coal and typically requires densification such as pelletization. Several key parameters including pelletization and torrefaction conditions were investigated in order to determine the effects on energy density of torrefied char pellets. In this study, multivariate analyses were performed using three independent variables, torrefaction temperature, pelletization temperature, and pelletization pressure; and five dependent variables: mass density, durability, volume expansion, higher heating value (HHV), and energy density (ED). The variation and correlation of the dependent variables were evaluated using principal component analysis (PCA). Additionally, multivariate linear regression was performed to correlate independent variables and dependent variable responses using an adjusted sum of squares method and a two-sided, 90% confidence interval for the mean response; the model was fitted with statistically significant linear, quadratic, and interaction terms. The statistical analysis showed that all independent parameters are statistically significant for ED with the largest model variation from experimental data being 1.44%. Response curve of the regression analysis showed that mass density, HHV, and energy density model have the highest output predictability with higher R2 values of 95.3%, 98.1%, and 74.1%, respectively.

The diffusion model belongs to the class of sequential sampling models. Based on information about RT distributions from binary tasks, the model allows the separation of distinct processes underlying decision-making. With the present simulation study, we demonstrate that diffusion modeling has essential advantages over the mere analysis of behavioral variables such as accuracy rate or mean RT. Specifically, our results show that qualitatively different sets of diffusion model parameters can lead to the same pattern of these aggregated variables, which renders the interpretation of these measures ambiguous. Even apparently clear patterns of results can be based on completely different parameter sets, which reflect completely different cognitive processes. For example, if one group\IeC {\textquoteright }s performance (or performance in one experimental condition) is superior in both mean RT and accuracy rate (i.e., fast responses and few errors), this does not necessarily mean that in this group (or condition) information processing is faster (drift rate of the diffusion model). Accordingly, we conclude that the mere analysis of behavioral variables can result in false conclusions.

This study aims to use structural equation modeling (SEM) to investigate the role of error processing in behavioral adaptation in children by testing relationships between error-related and stimulus-related event-related potentials (ERPs) obtained from two sessions of a speeded Eriksen flanker task. First, path models of averaged ERP components and mean response times (N1 → P2 → N2 → P3 → RTs) while controlling for trait effects, age, and sex, on each was examined separately for correct and incorrect trials from each session. While the model demonstrated acceptable fit statistics, the four models yielded diverse results. Next, path models for correct and incorrect trials were tested using latent variables defined by factoring together respective measures of ERP component amplitudes from each session. Comparison of correct and incorrect models revealed significant differences in the relationships between the successive measures of neural processing after controlling for trait effects. Moreover, latent variable models controlling for both trait and session-specific state variables yielded excellent model fit while models without session-specific state variables did not. In the final model, the error-related neural activity (i.e., the ERN and Pe) from incorrect trials was found to significantly relate to the stream of neural processes contributing to trials with the correct behavior. Importantly, the relationship between RT and error detection in the final model signifies a brain-and-behavior feedback loop. These findings provided empirical evidence that supports the adaptive orienting theory of error processing by demonstrating how the neural signals of error processing influence behavioral adaptations that facilitate correct behavioral performance.

Standard regression models focus on the mean response based on covariates. Quantile regression describes the quantile for a response conditioned to values of covariates. The relevance of quantile regression is even greater when the response follows an asymmetrical distribution. This relevance is because the mean is not a good centrality measure to resume asymmetrically distributed data. In such a scenario, the median is a better measure of the central tendency. Quantile regression, which includes median modeling, is a better alternative to describe asymmetrically distributed data. The Weibull distribution is asymmetrical, has positive support, and has been extensively studied. In this work, we propose a new approach to quantile regression based on the Weibull distribution parameterized by its quantiles. We estimate the model parameters using the maximum likelihood method, discuss their asymptotic properties, and develop hypothesis tests. Two types of residuals are presented to evaluate the model fitting to data. We conduct Monte Carlo simulations to assess the performance of the maximum likelihood estimators and residuals. Local influence techniques are also derived to analyze the impact of perturbations on the estimated parameters, allowing us to detect potentially influential observations. We apply the obtained results to a real-world data set to show how helpful this type of quantile regression model is.

The Gittins policy is a highly general scheduling policy that minimizes a wide variety of mean holding cost metrics in the M/G/1 queue. Perhaps most famously, Gittins minimizes mean response time in the M/G/1 when jobs' service times are unknown to the scheduler. Gittins also minimizes weighted versions of mean response time. For example, the well-known "$c\mu$ rule", which minimizes class-weighted mean response time in the multiclass M/M/1, is a special case of Gittins. However, despite the extensive literature on Gittins in the M/G/1, it contains no fully general proof of Gittins's optimality. This is because Gittins was originally developed for the multi-armed bandit problem. Translating arguments from the multi-armed bandit to the M/G/1 is technically demanding, so it has only been done rigorously in some special cases. The extent of Gittins's optimality in the M/G/1 is thus not entirely clear. In this work we provide the first fully general proof of Gittins's optimality in the M/G/1. The optimality result we obtain is even more general than was previously known. For example, we show that Gittins minimizes mean slowdown in the M/G/1 with unknown or partially known service times, and we show that Gittins's optimality holds under batch arrivals. Our proof uses a novel approach that works directly with the M/G/1, avoiding the difficulties of translating from the multi-armed bandit problem., Comment: Originally published at WiOpt 2021; this extended and revised version includes additional discussion throughout and fixes a minor error in Section VII

The need for industrial buildings has increased multifold, with a wide range in the irregular distribution of floor mass and stiffness. The uneven distribution of floor mass causes mass eccentricity in the floor, inducing torsion in the building. The aim of the present study is to highlight the influence of torsional irregularity due to mass eccentricity on the multi-directional floor response spectra. IS 1893 (Part 4): 2015 suggested time-history analysis of the design basis earthquake to develop floor response spectra, adopting linear analysis technique. Eleven sets of real orthogonal records were chosen to perform time-history analysis to obtain a mean response. The present study observed that the building’s with regular geometry and without stiffness variation in it, a slight mass eccentricity significantly increases floor response spectra. Moreover, actual industrial buildings have significant variations in mass, stiffness, and strength, resulting in further intensifying floor response spectra. Thus, the structural designer needs to use tri-directional floor response spectra for precise and protective non-structural component support design for appropriate response. IS 1893 (Part 1): 2016 suggests reduced stiffness for beam and column for earthquake analysis. Floor response spectra with reduced stiffness were also studied here, and the response was compared with non-linear analysis. Non-structural components lateral design forces as per floor response spectra approach and Indian Standard provisions are also included in the study.

The Chinese Lexicon Project is a repository of lexical decision data for 25,286 Cantonese Chinese two-character compound words. To create that repository, 594 participants responded to approximately 1,404 words and 1,404 nonwords over three sessions. Using the data in this repository, the present study examines the variability and reliability of Chinese lexical decision performance, along with the moderating influence of individual differences on lexical processing. We generally found high to very high within- and between-session reliabilities for mean response times, ex-Gaussian parameters, accuracy rates, and a composite proficiency measure tapping lexical processing fluency. Using linear mixed effects models, we also found reliable interactions between fluency and two lexical effects. Specifically, more fluent readers showed larger effects of word frequency and semantic transparency. These results attest to the stability of Chinese word recognition performance, and are most consistent with a flexible lexical processing system that adapts optimally to task demands.

In a series of experiments with undergraduate participants, we used a force-transducing device to determine how response force varies across different conjugate reinforcement and extinction conditions. In Experiment 1, we assessed how different parameters of response-contingent volume change in audiovisual stimuli maintained conjugate responding on a force transducer during conjugate reinforcement and extinction components. Response force and schedule force-multiplier values covaried, such that response force tended to decrease as force multipliers increased. Furthermore, proportionally greater response force was observed in extinction components following denser (i.e., 100x conjugate reinforcement) components compared to leaner (i.e., 5x conjugate reinforcement) components. In Experiment 2, we assessed the degree to which responding was maintained when conjugate changes in the audiovisual stimuli were delivered response-contingently or response-independently across reinforcement and extinction components. Response-independent reinforcer delivery yielded mixed results, but mean response force was proportionally higher in extinction sessions for all four participants following response-independent reinforcer delivery compared to extinction following response-contingent reinforcer delivery. Results from this project highlight the utility of the preparation described by Falligant, Rapp, Brogan, and Pinkston (2018) for studying response force in conjugate schedules to assess extinction-induced variability.

When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean response at the true but unknown input parameter, including point estimation and confidence interval construction. Risk quantification of mean response under input uncertainty often plays an important role in system evaluation and control, because it provides inferences on extreme scenarios of mean response in all possible input models. To the best of our knowledge, it has rarely been systematically studied in the literature. In this article, first we introduce risk measures of mean response under input uncertainty and propose a nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. We further study the associated budget allocation problem for efficient nested risk simulation and finally use a sharing economy example to illustrate the importance of accessing and controlling risk due to input uncertainty.

Understanding the relation between land-use types and baseflow mean response time (BMRT) is important to explore the response mechanism of baseflow processes in watersheds. BMRT was determined using an instantaneous unit hydrograph. The instantaneous unit hydrograph parameters were estimated by autocorrelation functions. The relative importance of land-use types in determining BMRT dynamics was assessed by hydrological model and partial least-squares regression. Our study suggests greater effects of urban area on BMRT than the effects of forest and agricultural land. This may be because the urban interception impervious area may impede baseflow generation over a short timescale. The effects of agricultural land are greater than those of forest in areas with steeper hillslopes, but lower than those of the forest in areas with more plains, reflecting the varied ability of forest and agricultural lands with different topography to hinder overland flow. Variations of BMRT are strongly linked to land use in the watershed. Overall, our study provides insight into the BMRT and dominant factors of land-use types in watersheds, planning of sustainable water resource use, and ecological protection in watersheds.

BackgroundVirtual digital assistants are devices that interact with the user through natural language processing and artificial intelligence. They can respond to verbal requests for first aid information. This study analyses the responses provided by the four most common devices.MethodsThis mixed-methods study employs structured interviews of the virtual digital assistants (Alexa, Cortana, Google Home and Siri) as well as descriptive statistical analyses. One hundred and twenty-three interview questions, based on 39 first aid topics, were employed. Responses were analysed for recognition and quality. Detection of query acuity was performed according to triage guidelines and response complexity was calculated.ResultsDevice performance was highly variable. Alexa and Google Home demonstrated high rates of recognition (92% vs 98% (p=0.03)) and low-to-moderate congruence with guidelines (19% vs 56% (p=0.04)). They appropriately recommended emergency response system activation 46% of the time vs 16% (p=0.01) of the time, respectively. The overall low quality responses of Cortana and Siri prohibited their analysis. Mean response complexity for Alexa was ‘grade 10’ vs ‘grade 8’ for Google Home (pInterpretationThis is the first study to assess virtual digital assistants from a first aid and basic life support perspective, finding potential in this technology to provide life-saving instructions and activate the emergency response system. When asked common first aid related questions Google Home and Alexa outperformed Siri and Cortana. Overall, the device responses were of mixed quality ranging from the provision of factual guideline-based information to no response at all.

The ternary optical computer (TOC) has attracted increasing attention from its providers and potential customers because of the advantages of its optical processor, such as low power consumption, numerous trits, parallelism, dynamical reconfigurability and bitwise allocability. The analysis of its performance has become an urgent problem to be solved in recent years. This paper builds a four-stage TOC service model by introducing synchronous multi-vacations and tandem queueing. Here, a vacation refers specifically to an optical processor vacation. Additionally, we propose a processor-divided-equally(PDE) strategy and a task scheduling and optical processor allocation algorithm under this strategy. Assuming that the intervals of task arrival follow a homogeneous Poisson process, we obtain some important performance indicators, such as the mean response time, mean number of tasks, and optical processor utilization under the PDE strategy, which are based on the M/M/1 and M/M/n queueing systems with exhaustive service and synchronous multi-vacations. The numerical results illustrate that the number of small optical processors has an important effect on the performance of TOC and that high vacation rate can improve the system performance to some extent.

We propose adaptive empirical likelihood (EL) estimators to improve estimation efficiency of existing adaptive estimators of the mean response with non-ignorable non-response data. The proposed ada...

Crucial to the success of studies based on capture-mark-recapture (CMR) designs is the retention (permanency) and recognition (readability) of marks to identify individuals. Several marking methods for small mammals (< 60 g) are available, but their efficacy and use is not well known. We implemented a targeted survey of experts to gather their experiences and opinions regarding marking small mammals. Respondents (n = 114) stated their beliefs, perceptions, and current and future use, of marking methods, as well as factors influencing their choices, based on Likert and rank order scale questions. We compared responses based on where researchers’ studies occurred, their level of experience, and their subfield of mammalogy. Most respondents (73%) had > 5 years experience marking small mammals, with 60% each marking > 1,000 individuals. Respondents believed that ear-tagging was most preferable in terms of efficiency, impact to affected animals (survival, pain), and personal ethics, whereas passive integrated transponder (PIT)-tagging was the most preferable with regard to retention and recognition, and toe-clipping with respect to cost. Most respondents plan to use ear-tagging (78%) or PIT-tagging (70%) in the future. PIT- and genetic-tagging are expected to increase, and toe-clipping to decline, in the future. The factors influencing which marking method respondents used were ranked—in order of decreasing preference—as impact, retention, recognition, cost, efficiency, and ethics. There were few differences in the mean response or consensus among respondents, regardless of their experience, location, or subfield. Most respondents (66%) agreed that additional studies on the performance and impact of various marking methods are needed to assess their costs and benefits for CMR-based studies. Ultimately, choice of marking method will depend on the species, research question, available resources, and local legislation and permitting. Our study, however, illustrates that collective insights by experienced mammalogists may aid individual researchers in deciding on study designs and protocols, particularly early career scientists.

Objectives The majority of subjects do not understand how to accurately report PRO data due to conceptual misunderstandings. This study demonstrates how even a short 2-sentence instruction can improve subject understanding. Methods For this study, 613 subjects completed an online survey, in which they were asked to provide responses to commonly seen PRO questions from various therapeutic areas. Demographic data were also collected. Results Subjects were provided with scenarios relating to pain severity, the definition of a rescue laxative, reporting stool counts, reporting a bleeding event, and itch severity. After subjects provided an initial response to the question, they were provided with minimal training information consisting of 1-2 sentences and asked to provide a response again to the same question. A 16% increase in mean response accuracy was found amongst all 5 questions evaluated by subjects. Conclusion Patient understanding of PRO items often seen as key endpoints in clinical trials was shown to increase with minimal training thus increasing the accuracy of data collected.

We investigated how different graphical representations convey the underlying uncertainty distribution in ensemble predictions. In ensemble predictions, a set of forecasts is produced, indicating the range of possible future states. Adopting a use case from life sciences, we asked non-expert participants to compare ensemble predictions of the growth distribution of individual children to that of the normal population. For each individual child, the historical growth data of a set of 20 of its best matching peers was adopted as the ensemble prediction of the child’s growth curve. The ensemble growth predictions were plotted in seven different graphical formats (an ensemble plot, depicting all 20 forecasts and six summary representations, depicting the peer group mean and standard deviation). These graphs were plotted on a population chart with a given mean and variance. For comparison, we included a representation showing only the initial part of the growth curve without any future predictions. For 3 months old children that were measured at four occasions since birth, participants predicted their length at the age of 2 years. They compared their prediction to either (1) the population mean or to (2) a “normal” population range (the mean ± 2(standard deviation)). Our results show that the interpretation of a given uncertainty visualization depends on its visual characteristics, on the type of estimate required and on the user’s numeracy. Numeracy correlates negatively with bias (mean response error) magnitude (i.e. people with lower numeracy show larger response bias). Compared to the summary plots that yield a substantial overestimation of probabilities, and the No-prediction representation that results in quite variable predictions, the Ensemble representation consistently shows a lower probability estimation, resulting in the smallest overall response bias. The current results suggest that an Ensemble or “spaghetti plot” representation may be the best choice for communicating the uncertainty in ensemble predictions to non-expert users.

One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which places larger weight on units closer to the threshold. We propose a Gaussian process regression methodology that acts as a Bayesian analog to LLR for regression discontinuity designs. Our methodology provides a flexible fit for treatment and control responses by placing a general prior on the mean response functions. Furthermore, unlike LLR, our methodology can incorporate uncertainty in how units are weighted when estimating the treatment effect. We prove our method is consistent in estimating the average treatment effect at the threshold. Furthermore, we find via simulation that our method exhibits promising coverage, interval length, and mean squared error properties compared to standard LLR and state-of-the-art LLR methodologies. Finally, we explore the performance of our method on a real-world example by studying the impact of being a first-round draft pick on the performance and playing time of basketball players in the National Basketball Association., 40 pages, 5 figures, 5 tables

Nowadays, dual response surface approach is used extensively, and it is known as one of the powerful tools for robust design. General assumptions are the data is normally distributed, and there is no outlier in the data set. The traditional procedures for robust design is to establish the process location and process scale models of the response variable based on sample mean and sample variance, respectively. Meanwhile, the ordinary least squares (OLS) method is often used to estimate the parameters of the regression response location and scale models. Nevertheless, many statistics practitioners are unaware that these existing procedures are easily influenced by outliers, and hence resulted in less accurate estimated mean response obtained through non-resistant method. As an alternative, the use of MM-location, MM-scale estimator, and MM regression estimator is proposed, in order to overcome the shortcomings of the existing procedures. This study employs a new penalty function optimization scheme to determine the optimum factor settings for robust design variables. The effectiveness of our proposed methods is confirmed by well-known example and Monte Carlo simulations.

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. We demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters., 21 pages, 6 composite figures. arXiv admin note: substantial text overlap with arXiv:1802.01487

The objective of this work was to compare several models of body weight data from 90-day rodent feeding trials. Polynomial and nonlinear functions relating time and weight were examined as were the use of Toeplitz error covariance structures and random coefficients. The models were evaluated by fitting them to five publicly available datasets from rat feeding studies. Model performance was assessed in terms of their ability to capture the complexity of the growth patterns, validity of necessary assumptions, and information criteria scores. The results demonstrated the importance of selecting a curve function that effectively reflects the mean response. Toeplitz error covariance structures resulted in superior model fit, while failing to address deviations from model assumptions. Models using the Richards function and random coefficients were generally superior to the other models evaluated and dramatically improved upon linear models with complex error structures.

Redundancy is an increasingly popular technique for reducing response times in computer systems, and there is a growing body of theoretical work seeking to analyze performance in systems with redundancy. The idea is to dispatch a job to multiple servers at the same time and wait for the first copy to complete service. Redundancy can help reduce response time because redundant jobs get to experience the shortest of multiple queueing times and potentially of multiple service times—but it can hurt jobs that are not redundant and must wait behind the redundant jobs’ extra copies. Thus in designing redundancy systems it is critical to find ways to leverage the potential benefits without incurring the potential costs. Scheduling represents one tool for maximizing the benefits of redundancy. In this paper we study three scheduling policies: First-Come First-Served (FCFS), Least Redundant First (LRF, under which less-redundant jobs have priority over more-redundant jobs), and Primaries First (PF, under which each job designates a “primary” copy, and all other copies have lowest priority). Our goal for each of these policies is to understand the marginal impact of redundancy: how much redundancy is needed to get the biggest benefit? We study this question analytically for LRF and FCFS, and via simulation for all three policies. One of our primary contributions is a surprisingly intricate proof that mean response time is convex as well as decreasing as the proportion of jobs that are redundant increases under LRF for exponential services. While response time under PF is also decreasing and appears to be convex as well, we find that, surprisingly, FCFS may be neither decreasing nor convex, depending on the parameter values. Thus, the scheduling policy is key in determining both whether redundancy helps and the marginal effects of adding more redundancy to the system.

In a variety of applications researchers are interested in comparing two or more naturally ordered experimental conditions after adjusting for covariates. Addressing this problem we develop a methodology for estimating a mean response conditional on covariates in the framework of partially linear models which allows the effects of some covariates to be modeled nonparametrically. Our focus is on univariate responses but extensions to multivariate response data are also considered. The new methodology is applied to data from a study that examined the relationship between exposure to PFASs, a class of widely used environmental pollutants, and plasma lipids in a cohort of pregnant women.

The statistical literature and practitioners have long advocated the use of confirmation experiments as the final stage of a sequence of designed experiments to verify that the optimal operating conditions identified as part of a response surface methodology strategy are attainable and able to achieve the value of the response desired. However, until recently there has been a gap between this recommendation and details about how to perform an analysis to quantitatively assess whether the confirmation runs are adequate. Similarly, there has been little in the way of specific recommendations for the number and nature of the confirmation runs that should be performed. In this article, we propose analysis methods to assess agreement between the mean response from previous experiments and the confirmation experiment, and suggest a strategy for the design of confirmation experiments that more fully explores the region around the optimum.