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In-clinic follow-up to assess completion of medical abortion is no longer a requirement according to World Health Organization guidance, provided adequate counselling is given. However, timely recognition of ongoing pregnancy, complications or incomplete abortion, which require treatment, is important. As part of a larger trial, this study aimed to establish whether women having a medical abortion could self-assess whether their abortion was complete using an automated, interactive questionnaire on their mobile phones. All 469 participants received standard abortion care and all returnees filled in a self-assessment on paper at clinic follow-up 2–3 weeks later. The 234 women allocated to receive the phone messages were also asked to do a mobile phone assessment at home ten days post-misoprostol. Completion of the mobile assessment was tracked by computer and all completed assessments, paper and mobile, were compared to providers’ assessments at clinic follow-up. Of the 226 women able to access the mobile phone assessment, 176 (78%) completed it; 161 of them (93%) reported it was easy to do so. Neither mobile nor paper self-assessments predicted all cases needing additional treatment at follow-up. Prediction of complete procedures was good; 71% of mobile assessments and 91% of paper assessments were accurate. We conclude that an interactive questionnaire assessing completion of medical abortion on mobile phones is feasible in the South African setting; however, it should be done later than day 10 and combined with an appropriate pregnancy test to accurately detect incomplete procedures. [ABSTRACT FROM AUTHOR]

The circular cartogram, also known as the Dorling map, is a widely used tool for visualizing statistical data. It represents regions as circles with their areas in proportion to the statistical values and requires circle displacement to avoid overlap and maintain spatial relationships. In this paper, we propose a new approach for circular cartogram production that utilizes the elastic beam displacement algorithm in cartographic generalization. First, the initial circles are generated with their areas in proportion to the statistical values. Second, an elastic beam structure is built as a proximity graph based on the spatial relations between the circles. Third, the circles violating the quality requirements are considered to have a force on the nodes of a beam. Fourth, the elastic beam algorithm is applied to assign forces for each node to determine the new positions of the circles. Steps two through four are repeated until a circular cartogram that meets the defined quality requirements is obtained. The experiments indicate that the proposed approach can successfully generate circular cartograms without overlaps while maintaining topology and relative relationships with higher quality than existing approaches. Project website: [ABSTRACT FROM AUTHOR]

Data preprocessing is an essential task to prepare suitable target datasets to apply statistical and data mining algorithms. It has become one of the complex segments in Web usage mining due to the massive and unstructured nature of Web server log. The data preprocessing segment in Web usage mining is divided into several phases such as data fusion, data cleaning, user identification, session identification, path completion, and data formatting. This paper focuses on the initial phases of the Web usage mining process, such as data cleaning, user identification, and session identification. Due to the growing size of log data at terabyte and petabyte scale, traditional data preprocessing algorithms fail at scalability points and possess Big Data issues. During the previous few years, the MapReduce framework has evolved as one of the most used parallel programming frameworks for processing Big Data on a cluster of nodes. In this paper, a MapReduce-based data preprocessing algorithm is developed. This algorithm comprises data preprocessing subphases such as data cleaning, user identification, and session identification. Various efficient heuristics are incorporated into existing MapReduce-based data preprocessing algorithm to detect ethical and unethical robots. Further several experiments are performed on a cluster of nodes and found that the proposed MapReduce-based data preprocessing algorithm is efficient and scalable for larger datasets. Moreover, we have also analyzed the impact of robots' requests on sessions generated in the session identification phase to measure the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel method combining electrolaryngography and acoustic analysis to detect the onset and offset of phonation as well as the beginning and ending of notes within a sung legato phrase, through the application of a peak-picking algorithm, TIMEX. The evaluation of the method applied to a set of singing duo recordings shows an overall performance of 78% within a tolerance window of 50 ms compared with manual annotations performed by three experts. Results seem very promising in light of the state-of-the-art techniques presented at MIREX in 2016 yielding an overall performance of around 60%. The new method was applied to a pilot study with two duets to analyse synchronization between singers during ensemble performances. Results from this investigation demonstrate bidirectional temporal adaptations between performers, and suggest that the precision and consistency of synchronization, and the tendency to precede or lag a co-performer might be affected by visual contact between singers and leader–follower relationships. The outcomes of this paper promise to be beneficial for future investigations of synchronization in singing ensembles. [ABSTRACT FROM AUTHOR]

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. [ABSTRACT FROM AUTHOR]

The paper proposes a multi-scale uncertainty quantification framework of composite laminated plates. The micro and macro uncertainty parameters are represented by random and interval variables according to available uncertainty information. The data-driven Polynomial Chaos Expansion model is constructed, where polynomial coefficients of random variables are determined based on their high-dimensional statistical information, and coefficients of interval variables are calculated using sampling analysis method. The proposed framework is applied for multi-scale buckling analysis, natural frequency calculation and reliability estimation. The comparison results with Monte Carlo Sampling method demonstrate the computational superiority of the proposed algorithm to achieve high accuracy in multiple conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new lossless encryption and decryption method for digital colour-image using 2D discrete Haar wavelet transform (DHWT) and 3D logistic chaotic map. The proposed cryptosystem provides the security of the digital images using the cryptographic algorithm in the frequency domain of the image. The proposed method uses two levels of 2D DHWT for the decomposition of the image. For the encryption, 2D DHWT is applied on the different colour components of the colour-image and then the encryption method is applied on the different sub-bands of each level of decomposition and for the decryption the reverse process of the encryption is used with the inverse of functions used in encryption. The experimental results and the security analysis like brute force analysis, statistical analysis, occlusion and differential attack of the proposed method are given to validate the feasibility and robustness of the method. [ABSTRACT FROM AUTHOR]

This paper outlines the notion of ‘algorithmic technique’ as a middle ground between concrete, implemented algorithms and the broader study and theorization of software. Algorithmic techniques specify principles and methods for doing things in the medium of software and they thus constitute units of knowledge and expertise in the domain of software making. I suggest that algorithmic techniques are a suitable object of study for the humanities and social science since they capture the central technical principles behind actual software, but can generally be described in accessible language. To make my case, I focus on the field of information ordering and, first, discuss the wider historical trajectory of formal or ‘mechanical’ reasoning applied to matters of commerce and government before, second, moving to the investigation of a particular algorithmic technique, theBayes classifier. This technique is explicated through a reading of the original work of M. E. Maron in the early 1960 and presented as a means to subject empirical, ‘datafied’ reality to aninterested readingthat confers meaning to each variable in relation to an operational goal. After a discussion of the Bayes classifier in relation to the question of power, the paper concludes by coming back to its initial motive and argues for increased attention to algorithmic techniques in the study of software. [ABSTRACT FROM PUBLISHER]

In practice, we often need to identify individuals whose longitudinal behaviour is different from the behaviour of those well-functioning individuals, so that some unpleasant consequences (e.g. stroke) can be avoided or early detected. To handle such applications, a new statistical method, called dynamic screening system, has been developed in the literature. A recent version of this method can analyze correlated data. However, the computation involved is intensive. In this paper, we suggest a fast computing algorithm for the dynamic screening system. The algorithm can improve the effectiveness of the conventional dynamic screening system in certain cases. Numerical results show that the new algorithm works well in different cases. [ABSTRACT FROM AUTHOR]

The stochastic realisation problem is associated with fitting a state-space model to a given data-set so that the second-order statistics of the output of the system match those of the data. This problem has been well studied and documented in the 1-D case, but unfortunately not so in the 2-D case, despite the similarities. Until now, the main reason behind the lack of 2-D stochastic realisation algorithms is the fact that there is a strong coupling between horizontal and vertical states, which are difficult to separate. The only known way to separate the states is to assume the model to be causal, recursive, and separable-in-denominator (CRSD). Nevertheless, there is currently no known algorithm that can solve the general 2-D stochastic realisation problem. Such problem arises naturally in image modelling, where, given an image, one needs to fit a 2-D Kalman filter model to it. In this paper, we introduce a 2-D stochastic realisation algorithm for state-space models in the general 2-D Roesser form without using the CRSD assumption. The algorithm constructs a positive real 2-D Kalman filter model. We test the algorithm with three case studies, one of which is an image example. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of detecting outliers and influential points and propose an indirect classical Mahalanobis distance-based method (ICMD) for multivariate datasets. Rousseeuw and Van Zomeren described outliers as those points that do not follow the pattern of the majority of the data; this description has been commonly accepted in the statistical literature. First, we update this description to build ICMD by integrating the following idea: the role of at least one point in the data-driven pattern will be affected greatly before and after excluding an outlier. Then, a similar idea is used to identify influential points. The resulting algorithms are given in detail. Two artificial datasets and three real datasets are applied to show that ICMD is robust, swamping-free, and masking-resistant. [ABSTRACT FROM AUTHOR]

Models for dealing with survival data in the presence of a cured fraction of individuals have attracted the attention of many researchers and practitioners in recent years. In this paper, we propose a cure rate model under the competing risks scenario. For the number of causes that can lead to the event of interest, we assume the polylogarithm distribution. The model is flexible in the sense it encompasses some well-known models, which can be tested using large sample test statistics applied to nested models. Maximum-likelihood estimation based on the EM algorithm and hypothesis testing are investigated. Results of simulation studies designed to gauge the performance of the estimation method and of two test statistics are reported. The methodology is applied in the analysis of a data set. [ABSTRACT FROM AUTHOR]

This paper introduces an alternating conditional expectation (ACE) algorithm: a non-parametric approach for estimating the transformations that lead to the maximal multiple correlation of a response and a set of independent variables in regression and correlation analysis. These transformations can give the data analyst insight into the relationships between these variables so that this can be best described and non-linear relationships uncovered. Using the Bayesian information criterion (BIC), we show how to find the best closed-form approximations for the optimal ACE transformations. By means of ACE and BIC, the model fit can be considerably improved compared with the conventional linear model as demonstrated in the two simulated and two real datasets in this paper. [ABSTRACT FROM AUTHOR]

The number of vulnerabilities discovered and reported during the recent decades is enormous, making an improved ranking and prioritization of vulnerabilities’ severity a major issue for information technology (IT) management. Although several methodologies for ranking and scoring vulnerabilities have been proposed, the Common Vulnerability Scoring System (CVSS) is the open standard with wide acceptance from the information security community. Recently, the Weighted Impact Vulnerability Scoring System (WIVSS) has been proposed as an alternative scoring methodology, which assigns different weights to impact factors of vulnerability in order to achieve higher diversity of values and thus improvement in flexibility of ranking in comparison to CVSS. The purpose of this paper is to expand the idea of WIVSS by defining the sets of weights which provide higher diversity of values. For this reason, an algorithm that finds all the possible combinations of optimal weights within a specified range and under certain constrains is presented. The algorithm results in 14 different combinations of impact weights that are applied to a sample of 20,496 vulnerabilities and statistically analyzed for associations among impact factors. The results suggest that one specific combination of impact weights can achieve highest diversity of values. [ABSTRACT FROM PUBLISHER]

The perception of the visual complexity of World Wide Web (Web) pages is a topic of significant interest. Previous work has examined the relationship between complexity and various aspects of presentation, including font styles, colours and images, but automatically quantifying this dimension of a web page at the level of the document remains a challenge. In this paper we demonstrate that areas of high complexity can be identified by detecting areas, or ‘chunks’, of a web page high in block-level elements. We report a computational algorithm that captures this metric and places web pages in a sequence that shows an 86% correlation with the sequences generated through user judgements of complexity. The work shows that structural aspects of a web page influence how complex a user perceives it to be, and presents a straightforward means of determining complexity through examining the DOM. [ABSTRACT FROM PUBLISHER]

This paper reports the results of a study designed to determine whether there are statistically significant differences between the values of ambulatory blood pressure monitoring (ABPM) parameters obtained using different methods-fixed schedule, diary, and automatic algorithm based on actigraphy-of defining the main activity and rest periods, and to determine the clinical relevance of such differences. We studied 233 patients (98 men/135 women), 61.29 ± .83 yrs of age (mean ± SD). Statistical methods were used to measure agreement in the diagnosis and classification of subjects within the context of ABPM and cardiovascular disease risk assessment. The results show that there are statistically significant differences both at the group and individual levels. Those at the individual level have clinically significant implications, as they can result in a different classification, and, therefore, different diagnosis and treatment for individual subjects. The use of an automatic algorithm based on actigraphy can lead to better individual treatment by correcting the accuracy problems associated with the fixed schedule on patients whose actual activity/rest routine differs from the fixed schedule assumed, and it also overcomes the limitations and reliability issues associated with the use of diaries. (Author correspondence: ) [ABSTRACT FROM AUTHOR]

In this paper, an alternative method for the comparison of two diagnostic systems based on receiver operating characteristic (ROC) curves is presented. ROC curve analysis is often used as a statistical tool for the evaluation of diagnostic systems. However, in general, the comparison of ROC curves is not straightforward, in particular, when they cross each other. A similar difficulty is also observed in the multi-objective optimization field where sets of solutions defining fronts must be compared with a multi-dimensional space. Thus, the proposed methodology is based on a procedure used to compare the performance of distinct multi-objective optimization algorithms. In general, methods based on the area under the ROC curves are not sensitive to the existence of crossing points between the curves. The new approach can deal with this situation and also allows the comparison of partial portions of ROC curves according to particular values of sensitivity and specificity of practical interest. Simulations results are presented. For illustration purposes, considering real data from newborns with very low birthweight, the new method was applied in order to discriminate the better index for evaluating the risk of death. [ABSTRACT FROM AUTHOR]

The aim of this paper is to find an optimal alternative bivariate ranked-set sample for one-sample location model bivariate sign test. Our numerical and theoretical results indicated that the optimal designs for the bivariate sign test are the alternative designs with quantifying order statistics with labels {((r+1)/2, (r+1)/2)}, when the set size r is odd and {(r/2+1, r/2), (r/2, r/2+1)} when the set size r is even. The asymptotic distribution and Pitman efficiencies of these designs are derived. A simulation study is conducted to investigate the power of the proposed optimal designs. Illustration using real data with the Bootstrap algorithm for P-value estimation is used. [ABSTRACT FROM AUTHOR]

This paper examines the robustness of fractional integration estimates to different data frequencies. We show by means of Monte Carlo experiments that if the number of differences is an integer value (e.g. 0 or 1), there is no distortion when data are collected at wider intervals; however, if it is a fractional value, the distortion increases as the number of periods between the observations increases, which results in lower orders of integration than those of the true DGP. An empirical application using the S&P 500 index is also carried out. [ABSTRACT FROM AUTHOR]

In this paper, we consider the family of skew generalized t (SGT) distributions originally introduced by Theodossiou [P. Theodossiou, Financial data and the skewed generalized t distribution, Manage. Sci. Part 1 44 (12) ( 1998), pp. 1650-1661] as a skew extension of the generalized t (GT) distribution. The SGT distribution family warrants special attention, because it encompasses distributions having both heavy tails and skewness, and many of the widely used distributions such as Student's t, normal, Hansen's skew t, exponential power, and skew exponential power (SEP) distributions are included as limiting or special cases in the SGT family. We show that the SGT distribution can be obtained as the scale mixture of the SEP and generalized gamma distributions. We investigate several properties of the SGT distribution and consider the maximum likelihood estimation of the location, scale, and skewness parameters under the assumption that the shape parameters are known. We show that if the shape parameters are estimated along with the location, scale, and skewness parameters, the influence function for the maximum likelihood estimators becomes unbounded. We obtain the necessary conditions to ensure the uniqueness of the maximum likelihood estimators for the location, scale, and skewness parameters, with known shape parameters. We provide a simple iterative re-weighting algorithm to compute the maximum likelihood estimates for the location, scale, and skewness parameters and show that this simple algorithm can be identified as an EM-type algorithm. We finally present two applications of the SGT distributions in robust estimation. [ABSTRACT FROM AUTHOR]

Because of properties in chaos system such as the sensitive dependence on initial conditions, system parameters, pseudorandom property and ergodicity, chaotic image encryption algorithm can suggest a new and efficient way of encryption scheme, which has been studied more and more in recent years. A novel chaotic image encryption algorithm based on Toeplitz matrix and Hankel matrix is proposed in this paper. We shuffle totally the positions of image pixels to confuse the relationship between the plain image and cipher image combined with Toeplitz matrix, Hankel matrix and logistic chaotic system. Another hyper-chaos system of Chen's chaotic system is taken to change the grey values of image pixels to enhance the security further. Experimental results in Sections 3 and 4 demonstrate that the key space is large enough and the key is sensitive to initial conditions to resist the brute force attack in the proposed algorithm. Additionally, the distribution of grey values in encrypted image has a random-like behaviour to resist statistical analysis. [ABSTRACT FROM AUTHOR]

Locally stationary wavelet (LSW) processes, built on non-decimated wavelets, can be used to analyse and forecast non-stationary time series. They have been proved useful in the analysis of financial data. In this paper, we first carry out a sensitivity analysis, then propose some practical guidelines for choosing the wavelet bases for these processes. The existing forecasting algorithm is found to be vulnerable to outliers, and a new algorithm is proposed to overcome the weakness. The new algorithm is shown to be stable and outperforms the existing algorithm when applied to real financial data. The volatility forecasting ability of LSW modelling based on our new algorithm is then discussed and shown to be competitive with traditional GARCH models. [ABSTRACT FROM AUTHOR]

The generalized standard two-sided power (GTSP) distribution was mentioned only in passing by Kotz and van Dorp Beyond Beta, Other Continuous Families of Distributions with Bounded Support and Applications, World Scientific Press, Singapore, 2004. In this paper, we shall further investigate this three-parameter distribution by presenting some novel properties and use its more general form to contrast the chronology of developments of various authors on the two-parameter TSP distribution since its initial introduction. GTSP distributions allow for J-shaped forms of its pdf, whereas TSP distributions are limited to U-shaped and unimodal forms. Hence, GTSP distributions possess the same three distributional shapes as the classical beta distributions. A novel method and algorithm for the indirect elicitation of the two-power parameters of the GTSP distribution is developed. We present a Project Evaluation Review Technique example that utilizes this algorithm and demonstrates the benefit of separate powers for the two branches of activity GTSP distributions for project completion time uncertainty estimation. [ABSTRACT FROM AUTHOR]

Although thousands of quantitative structure-activity and structure-property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses. [ABSTRACT FROM AUTHOR]

In this paper, a novel state-based dynamic multi-alphabet arithmetic coding algorithm which adapts efficiently to the locally occurring symbol statistics is presented. The proposed coding algorithm is applicable to sources such as raw images or transformed images that locally produce a smaller set of symbols from a large alphabet, or any other source characterized by very large alphabet size and highly skewed distributions. The performance of the proposed algorithm is compared with two standard entropy coding schemes, CA-2D-VLC and CABAC, that have recently appeared in the literature, in terms of compression ratio, peak signal-to-noise ratio and subjective quality. The simulation results obtained are encouraging, paving the way for further research and hardware implementation of this algorithm. It is found that the proposed algorithm achieves an increase in the compression ratio of about 45% over the compared standards for the similar peak signal-to-noise ratio and subjective quality. [ABSTRACT FROM AUTHOR]

A segmented line regression model has been used to describe changes in cancer incidence and mortality trends [Kim, H.-J., Fay, M.P., Feuer, E.J. and Midthune, D.N., 2000, Permutation tests for joinpoint regression with applications to cancer rates. Statistics in Medicine, 19, 335-351. Kim, H.-J., Fay, M.P., Yu, B., Barrett., M.J. and Feuer, E.J., 2004, Comparability of segmented line regression models. Biometrics, 60, 1005-1014.]. The least squares fit can be obtained by using either the grid search method proposed by Lerman [Lerman, P.M., 1980, Fitting segmented regression models by grid search. Applied Statistics, 29, 77-84.] which is implemented in Joinpoint 3.0 available at http://srab.cancer.gov/joinpoint/index.html, or by using the continuous fitting algorithm proposed by Hudson [Hudson, D.J., 1966, Fitting segmented curves whose join points have to be estimated. Journal of the American Statistical Association, 61, 1097-1129.] which will be implemented in the next version of Joinpoint software. Following the least squares fitting of the model, inference on the parameters can be pursued by using the asymptotic results of Hinkley [Hinkley, D.V., 1971, Inference in two-phase regression. Journal of the American Statistical Association, 66, 736-743.] and Feder [Feder, P.I., 1975a, On asymptotic distribution theory in segmented regression Problems-Identified Case. The Annals of Statistics, 3, 49-83.] Feder [Feder, P.I., 1975b, The log likelihood ratio in segmented regression. The Annals of Statistics, 3, 84-97.] Via simulations, this paper empirically examines small sample behavior of these asymptotic results, studies how the two fitting methods, the grid search and the Hudson's algorithm affect these inferential procedures, and also assesses the robustness of the asymptotic inferential procedures. [ABSTRACT FROM AUTHOR]

Sampling the correlation matrix (R) plays an important role in statistical inference for correlated models. There are two main constraints on a correlation matrix: positive definiteness and fixed diagonal elements. These constraints make sampling R difficult. In this paper, an efficient generalized parameter expanded re-parametrization and Metropolis-Hastings (GPX-RPMH) algorithm for sampling a correlation matrix is proposed. Drawing all components of R simultaneously from its full conditional distribution is realized by first drawing a covariance matrix from the derived parameter expanded candidate density (PXCD), and then translating it back to a correlation matrix and accepting it according to a Metropolis-Hastings (M-H) acceptance rate. The mixing rate in the M-H step can be adjusted through a class of tuning parameters embedded in the generalized candidate prior (GCP), which is chosen for R to derive the PXCD. This algorithm is illustrated using multivariate regression (MVR) models and a simulation study shows that the performance of the GPX-RPMH algorithm is more efficient than that of other methods. [ABSTRACT FROM AUTHOR]

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old. [ABSTRACT FROM AUTHOR]

This paper explores the potential of an artificial immune-based supervised classification algorithm for land-cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non-self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune-based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune-based classifier works well in comparison with the maximum likelihood and the decision-tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision-tree classification algorithms. Results suggest an improved performance with the immune-based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune-based supervised classifier requires several user-defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant. [ABSTRACT FROM AUTHOR]

This article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). The approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. The results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes. [ABSTRACT FROM AUTHOR]

This paper studies the causality conditions and autocovariance calculations in periodic vector auto-regressions. A state-space representation is exploited to extract a simple, necessary and sufficient condition for periodic stationarity, under which the autocovariances can be expressed in a simple but tractable form. The proposed methods compute the autocovariances for distinct seasons separately, thereby facilitating efficient computation for models with a large period. [ABSTRACT FROM AUTHOR]

This paper proposes a two-sided monitoring algorithm of changes in the exponential family. We adopt a Bayesian approach which uses a loss function and takes into account the run length of the process. It also extends the monitoring scheme proposed in [Harrison, P.J., Veerapen, P.P., 1994, A Bayesian decision approach to model monitoring and Cusums. Journal of Forecasting, 13, 29-36.] The algorithm is illustrated with several examples, which analyze the power of the monitor. [ABSTRACT FROM AUTHOR]

This paper focuses on deriving explicit formulae of the asymptotic variance in nonparametric regression with fractional time-series errors. Unified formulae are obtained for fixed design models with long-memory, short-memory and antipersistent errors. It is also found that in strongly antipersistent case the Uniform kernel is no longer the minimum variance one and that for a fourth-order kernel the constant in the asymptotic variance for long-memory errors may be clearly smaller than that for i.i.d. errors. The results are applied to improving an existing data-driven algorithm. Practical performance of the proposed algorithm is illustrated with simulated and real data examples. [ABSTRACT FROM AUTHOR]

The paper proposes a new approach to find an autoregressive moving average (ARMA) model order. The basic idea is to extend the previous approach proposed by Liang et al . to third order statistics (TOS). The algorithm uses data matrices rather than calculating cumulants of the observed signal. Hence, we avoid the non-stationary effects, which is due to finite-length observations. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian process. The input signal is unobservable. The observed sequence is corrupted by a zero-mean additive Gaussian noise. Examples are given to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Given a graph G and some property P ( g ), a P-ranking (ordering) of the nodes of G can be defined as a one-to-one function from V to {1, 2, 3, ..., n } such that property P( G ) holds for each node i ? V . In this paper we present an O ( n 2 ) self-stabilizing algorithm which, when given a rooted tree T , will provide P-rankings consistent with the following standard graph traversal properties: (i) preorder traversal; (ii) postorder traversal; (iii) reverse-postorder traversal; (iv) breadth-first traversal; (v) breadth–depth traversal. [ABSTRACT FROM AUTHOR]

Missing data occur in virtually all research. Traditional methods for handling missing data, like complete case analyses and mean substitution, can result in the loss of statistical power and biased estimates of parameters. Recent statistical and software developments have provided readily accessible methods of handling missing data that provide more statistical power and less biased estimates than in the past. This paper compares newer techniques and the statistical programs that employ these techniques for imputing data in structural equation modeling analysis. The techniques presented include the expectation-maximization (EM) algorithm, multiple imputations (Ml), and raw maximum- likelihood (RML), as employed in the statistical programs EMCOV, NORM, Amos, and Mplus. The assumptions, benefits, and limitations of each method and accompanying statistical programs are discussed. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are heuristic stochastic search and optimization techniques with principles taken from natural genetics. They are procedures mimicking the evolution process of an initial population through genetic transformations. This paper is concerned with the problem of finding A-optimal incomplete block designs for multiple treatment comparisons represented by a matrix of contrasts. An evolutionary algorithm for searching optimal, or nearly optimal, incomplete block designs is described in detail. Various examples regarding the application of the algorithm to some well-known problems illustrate the good performance of the algorithm [ABSTRACT FROM AUTHOR]

The self-organizing feature map SOFM) is primarily used to map high dimensional data into low dimensional spaces for pattern classification applications. The pre-defined connections in the SOFM lattice and the weight adaptation algorithm enable topological associations to emerge within arbitrary numeric data. The degree of association or similarity between neighboring nodes on the lattice is largely influenced by mathematical and statistical measures between the data vectors assigned to the nodes. The relationship between neighboring nodes, or cluster units, can be visually interpreted by an observer if this information is displayed as colors and/or distortions on the SOFM lattice. This paper describes how a SOFM that starts as a tessellated unit sphere can develop a closed surface topology of arbitrary N dimensional data vectors that reflects information content as defined by the mathematical or statistical measure. Transforming the numeric data into a closed geometric form enables the information embedded in large high dimensional data sets to be easily transferred into an immersive 3D virtual reality environment for interactive scientific data visualization. The implementation of the proposed methodology is illustrated using both high-dimensional synthetic data and the more common Fisher's Iris data. [ABSTRACT FROM AUTHOR]

This paper introduces practical methods of parameter and standard error estimation for adaptive robust regression where errors are assumed to be from a normal/independent family of distributions. In particular, generalized EM algorithms (GEM) are considered for the two cases of t and slash families of distributions. For the t family, one step method is proposed to estimate the degree of freedom parameter. Use of empirical information is suggested for standard error estimation. It is shown that this choice leads to standard errors that can be obtained as a by-product of the GEM algorithm. The proposed methods, as discussed, can be implemented in most available nonlinear regression programs. Details of implementation in SAS NLIN are given using two specific examples. [ABSTRACT FROM AUTHOR]

Manufacturing-related have traditionally made independence assumptions on associated stochastic processes in order to achieve tractability of analytical models and simplify Monte Carlo models. This paper aims to alert users to potential deleterious implications stemming from unfounded independence assumptions in traditional stochastic models of manufacturing systems. Specifically, it demonstrates the dramatic impact that appreciable autocorrelations can have on manufacturing performance measures through a preliminary study of prediction errors incurred in ignoring dependence. To this end, the study compared performance measures of common manufacturing models with renewal components to their autocorrelated counterparts, drawn From the TES (Transform-Expand-sample) class. TES models constitute a versatile class of stochastic processes, designed to capture empirical distributions and autocorrelation. simultaneously, and as such, are suitable for both Monte Carlo simulation and analytical modeling of autocorrelated time series. A brief overview of simple TES processes and their generation algorithms is also included. [ABSTRACT FROM AUTHOR]

In this paper we present a new, and extremely fast, algorithm for the inverse kinematics of discretely actuated manipulator arms with many degrees of freedom. Our only assumption is that the arm is macroscopically serial in structure, meaning that the overall structure is a serial cascade of units with each unit having either a serial or parallel kinematic structure. Our algorithm builds on previous works in which the authors and coworkers have used the workspace density function in a breadthfirst search for solving the inverse kinematics problem. The novelty of the method presented here is that only the 'mean' of this workspace density function is used. Hence the requirement of storing a sampled version of the workspace density function (which is a function on a six-dimensional space in the case of a spatial manipulator) is circumvented. We illustrate the technique with both planar revolute and variable-geometry-truss manipulators, and briefly describe a new manipulator design for which this algorithm is applicable. [ABSTRACT FROM AUTHOR]

This article presents the Markov chain Monte Carlo (MCMC) statistical technique. The impact on the discipline is deep and durable, because these methods have opened new horizons in the scale of the problems that one can deal with, thus enhancing the position of statistics in most applied fields. The MCMC revolution has in particular boosted Bayesian statistics to new heights by providing a virtually universal tool for dealing with integration problems. This can be seen in the explosion of papers dealing with complex models, hierarchical modelings, nonparametric Bayesian estimation, and spatial statistics. This trend has also created new synergies with mathematicians and probabilists, as well as econometricians, engineers, ecologists, astronomers, and others, for theoretical requests and practical implications of MCMC techniques. The main factor in the success of MCMC algorithms is that they can be implemented with little effort in a large variety of settings. This is obviously true of the Gibbs sampler, which provided some conditional distributions are available as shown by the BUGS software. We mentioned BUGS and CODA as existing software dedicated to MCMC algorithms but much remains to be done before MCMC becomes part of commercial software.

The article presents a rejoinder to comments made on their article "A Statistical Model for Position Emission Tomography." Being a very active, multidisciplinary, field of research, it is hard to keep track of who suggested what and in what context, so in attempting to answer scholar Donald B. Rubin's question we are likely to overlook some contributors. The first use of a pixel model for an organ appeared in scholar G.N. Hounsfield's original development of X-ray computerized tomography. In 1978 scholars gave a short account of this early development in the field, including images produced using Hounsfield's algorithms. The fact that the physics of Position Emission Tomography (PET) is different, and inherently more stochastic, than that of X-ray transmission tomography has been common knowledge. Realizing that the additional randomness of PET calls for a more statistical way of thinking, scholars A.J. Rockmore and A.Macovski suggested estimating the emission density in PET, using a maximum-likelihood approach. Their paper fell short of identifying the connection between the tube count and the emission density. This omission on their part is probably a result of being unaware of the development of statistical methods for incomplete data.

This paper presents a piecewise regression method for continuous models containing max or min operators, or both. This method does not require knowledge of the zone in which a shift in regimes occurs. Moreover, it allows the application of analytical derivatives to maximize the likelihood function, which greatly simplifies the estimation of the model. The method proposed exhibits fast convergence and can be used for an arbitrary number of regimes and variables. [ABSTRACT FROM AUTHOR]

In this article, the author presents his views on a paper by David Oakes on statistical analysis. Oakes's paper demonstrates the impossibility of constructing a joint distribution for an infinite sequence of events that could be guaranteed to have the calibration property for any possible sequence of outcomes. Imagine a statistician attempting to model the sequence of events, which she observes sequentially. She might start off with a simple Bernoulli model. For any currently adopted model, she could form estimates of its parameters based on current data and use these to provide a forecast probability for the next event. The author argues that any such statistical analysis must be regarded as empirically invalidated unless its infinite sequence of forecasts is calibrated. Consequently, Oakes's result shows that, no matter how cleverly one tries to tune model-building, testing, estimation procedures, and so on, there is no guarantee of success. There will always be data sequences that will be impossible to track. It is further shown that, for any data sequence, any two computable distributions that are both suitably calibrated must give forecasts whose differences tend to zero, so that calibrated forecasts are "asymptotically unique" and so, in a sense, objectively valid.

Statisticians like Good and Gaskins have given to bump-hunters an inventive approach that uses the special structure of their maximum penalized-likelihood, MPL procedure. This article demonstrates the usefulness of nonparametric density-estimation techniques for unearthing features such as modes in the body of the data without concern for possible tail pathologies. With medical data the estimation and comparison of modes has proved more revealing than simpler tests. For example, the discovery of bimodal bivariate density functions in lipid data has led to clarification of the roles of certain cardiovascular risk factors. Investigators interested in the statistical significance of modes should also find Good and Gaskins's paper useful. The important algorithms presented in this paper deal with optimization of the smoothness or variance-bias trade-off of the density estimate and with removal of potential bumps by surgery.