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Advances In Multivariate Statistical Methods [Kõva köide]

Edited by (Indian Statistical Inst, India)
Teised raamatud teemal:
Teised raamatud teemal:
The Indian Statistical Institute (ISI), established in 1931, held a workshop and conference in December 2006, celebrating 75 years and also honoring Professor S.N. Roy, an eminent statistician. Presentations from that conference are being published in a series of volumes, of which this is number four. Emerging applications include bioinformatics, categorical data and clinical trials, econometrics, longitudinal data analysis, and microarray data analysis, among others. Twenty-seven papers deal with the analysis of large dimensional data, models and inference for directional data, characterization using variate distribution theory, estimation, testing of hypotheses problems, Bayesian methods and their impact on analysis of multivariate data, multivariate statistical methods in several real-life applications, reliability analysis, analysis of some special types of time series data, and multivariate methods in sample surveys. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

This volume contains a collection of research articles on multivariate statistical methods, encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. It serves as a tribute to Professor S N Roy, an eminent statistician who has made seminal contributions to the area of multivariate statistical methods, on his birth centenary. In the area of emerging applications, the topics include bioinformatics, categorical data and clinical trials, econometrics, longitudinal data analysis, microarray data analysis, sample surveys, statistical process control, etc.Researchers, professionals and advanced graduates will find the book an essential resource for modern developments in theory as well as for innovative and emerging important applications in the area of multivariate statistical methods.
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.