ADVANCES IN MULTIVARIATE STATISTICAL METHODS

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• High-Dimensional Discrete Statistical Models; Multivariate Theory for High-Dimensional Data with Fewer Observations; Model-based Penalized Clustering for Multivariate Data; Jacobians under Constraints and Statistical Bioinformatics; Cluster Validation for Microarray Data; Flexible Bivariate Circular Models; Optimal Text Space Representation Through Circular Data Analysis; Linear Regression for Random Measures; Mixed Multivariate Models for Random Sums and Maxima; Estimation of the Box-Cox Transformation Parameters; Generation of Multivariate Densities; Smooth Estimation of Multivariate Distribution and Density and Functions; Estimation Using Quantile Function Structure; Optimal Estimating Functions in the Presence of Nuisance Parameters; Inference in Exponential Family Regression Models Under Shape Constraints; Optimal Adaptive Rule in Testing Problem; Robust Tests for Inverse Gaussian Scale Parameters; Clusterwise Regression Using Dirichlet Mixtures; Bayesian Analysis of Rank Data; Bayesian Tests of Equality of Stratified Proportions for a Multiple-Response Categorical Variable; Respondent-Generated Intervals in Sample Surveys; Quality Index and Mahalanobis D2 Statistic; AQL-based Multiattribute Sampling Scheme; Multivariate Quality Management; Large Time Series of Categorical Data; Estimation of Integrated Covolatility for Asynchronous Assets; Improving the Hansen-Hurwitz Estimator in PPSWR Sampling.