Journal of Data Science

Abstract:In this paper, for evaluating and comparing the heterogeneous balance-variation order pair of any two decision-making trial and evaluation laboratory (DEMATEL) theories, in which one has larger balance and smaller variation, and on the contrary, the other one has smaller balance and larger variation, the first author proposed a useful integrated validity index to evaluate any DEMATEL theory by combining Liu's balanced coefficient and Liu's variation coefficient .Using this new validity index, three kinds of DEMATELs with a same direct relational matrix, including the traditional DEMATEL, shrinkage DEMATEL and balance DEMATEL, are compared, a simple validity experiment is conducted, the results show that the balance DEMATEL has the best performance, the performance of the shrinkage coefficient is better than that of the traditional DEMATEL.

Abstract: Graphs are a great aid in interpreting multidimensional data. Two examples are employed to illustrate this point. In the first the many dissimilarities generated in the Analytic Network Process (ANP) are anal ysed using Individual Differences Scaling (INDSCAL). This is the first time such a procedure has been used in this context. In the second the single set of dissimilarities that arise from the Analytic Hierarchy Process (AHP) are analysed using Multidimensional Scaling (MDS). The novel approach adopted here replaces a complex iterative procedure with a systematic ap proach that may be readily automated.

Abstract: The traditional approach by Fama and Macbeth (1973) to the validity of an asset pricing model suffers from two drawbacks. Firstly, it uses the ordinary least squares (OLS) method, which is sensitive to outliers, to estimate the time-series beta. Secondly, it takes averages of the slope coefficients from cross-sectional regressions which ignore the importance of time-series properties. In this article, robust estimators and a longitudinal approach are applied to avoid the problems of these two kinds. We use data on the electronics industry in Taiwan’s stock market during the period from September 1998 to December 2001 in order to examine whether betas from the Capital Asset Pricing Model (CAPM) are a valid measure of risk and whether industries to which the firms belong explain excess returns. The methods we propose lead to more explanatory power than the traditional OLS results.

Abstract: Response variables that are scored as counts, for example, number of mastitis cases in dairy cattle, often arise in quantitative genetic analysis. When the number of zeros exceeds the amount expected such as under the Poisson density, the zero-inflated Poisson (ZIP) model is more appropriate. In using the ZIP model in animal breeding studies, it is necessary to accommodate genetic and environmental covariances. For that, this study proposes to model the mixture and Poisson parameters hierarchically, each as a function of two random effects, representing the genetic and environmental sources of variability, respectively. The genetic random effects are allowed to be correlated, leading to a correlation within and between clusters. The environmental effects are introduced by independent residual terms, accounting for overdispersion above that caused by extra-zeros. In addition, an inter correlation structure between random genetic effects affecting mixture and Poisson parameters is used to infer pleiotropy, an expression of the extent to which these parameters are influenced by common genes. The methods described here are illustrated with data on number of mastitis cases from Norwegian Red cows. Bayesian analysis yields posterior distributions useful for studying environmental and genetic variability, as well as genetic correlation.

Abstract: A new rank-based test statistics are proposed for the problem of a possible change in the distribution of independent observations. We extend the two-sample test statistic of Damico (2004) to the change point setup. The finite sample critical values of the proposed tests is estimated. We also conduct a Monte Carlo simulation to compare the powers of the new tests with their competitors. Using the Nile data of Cobb (1978), we demonstrate the applicability of the new tests.

In this paper, we introduce some new families of generalized Pareto distributions using the T-R{Y} framework. These families of distributions are named T-Pareto{Y} families, and they arise from the quantile functions of exponential, log-logistic, logistic, extreme value, Cauchy and Weibull distributions. The shapes of these T-Pareto families can be unimodal or bimodal, skewed to the left or skewed to the right with heavy tail. Some general properties of the T-Pareto{Y} family are investigated and these include the moments, modes, mean deviations from the mean and from the median, and Shannon entropy. Several new generalized Pareto distributions are also discussed. Four real data sets from engineering, biomedical and social science are analyzed to demonstrate the flexibility and usefulness of the T-Pareto{Y} families of distributions.

Abstract: Background: A fixed effects meta-analysis of ten exercise training in trials heart failure patients was conducted. The aim of this current work was to compare different approaches to meta-analysis using the same dataset from the previous work on ten exercise training trials in heart failure patients. Methods: The following different meta-analysis techniques were used to analyse the data and compared the effects of exercise training on BNP, NT pro-BNP and peak VO2 before and after exercise training: (1) Trial level (traditional) level MA i) Follow up (post-exercise training intervention) outcome only. ii) Baseline-follow up difference (2) Patient level MA by Post-Stage ANCOVA i)naive model does not take into account trial level ii) Single Stage iii) Two Stage (3) Post outcome only i) Single stage ii) Pre-post outcome difference Single stage Results: The Individual patient data (IPD) analyses produced smaller effect sizes and 95% confidence intervals compared to conventional meta analysis. The advantage of the one-stage model is that it allows sub-group analyses, while the two-stage model is considered more robust but limited for sub-analyses. Conclusions: Our recommendation is to use one-stage or two-stage ANCOVA analysis, the former allows sub-group analysis, while the latter is considered to be more technically robust.

In this article, the maximum likelihood estimators of the k independent exponential populations parameters are obtained based on joint progressive type- I censored (JPC-I) scheme. The Bayes estimators are also obtained by considering three different loss functions. The approximate confidence, two Bootstrap confidence and the Bayes credible intervals for the unknown parameters are discussed. A simulated and real data sets are analyzed to illustrate the theoretical results.

Abstract: The aim of this study is to develop a method for detection of temporomandibular disorder (TMD) based on visual analysis of facial movements. We analyse the motion of colour markers placed on the locations of interest on subjects faces in the video frames. We measured several features from motion patterns of the markers that can be used to distinguish between different classes. In our approach, both static and dynamic features are measured from a number of time sequences for classification of the subjects. A measure of nonlinear dynamics of the variations in the movement of colour markers positioned on the subjects faces was obtained via estimating the maximum Lyapunov exponent. Static features such as the number of outliers and kurtosis have also been evaluated. Then, Support Vector Machines (SVMs) are used to automatically classify all the subjects as belonging to individuals with TMD and healthy subjects.