Multivariate Statistics : Theory and Applications : proceedings of IX Tartu Conference on Multivariate Statistics and XX International Workshop on Matrices and Statistics, Tartu, Estonia, 26 June-1 July, 2011 / editor, Tonu Kollo, University of Tartu, Estonia.

Format:

Book

Conference/Event

Contributor:

Kollo, Tõnu, editor.

Conference Name:

Tartu Conference on Multivariate Statistics (9th : 2011 : Tartu, Estonia), issuing body.

International Workshop on Matrices and Statistics (20th : 2011 : Tartu, Estonia), issuing body.

Series:

Gale eBooks

Language:

English

Subjects (All):

Multivariate analysis--Congresses.

Multivariate analysis.

Statistics--Congresses.

Statistics.

Physical Description:

1 online resource (x, 167 pages) : illustrations (some color)

Place of Publication:

New Jersey : World Scientific, [2013]

Language Note:

English

Summary:

The book aims to present a wide range of the newest results on multivariate statistical models, distribution theory and applications of multivariate statistical methods. A paper on Pearson-Kotz-Dirichlet distributions by Professor N Balakrishnan contains main results of the Samuel Kotz Memorial Lecture. Extensions of linear models to multivariate exponential dispersion models and Growth Curve models are presented, and several papers on classification methods are included. Applications range from insurance mathematics to medical and industrial statistics and sampling algorithms.

Contents:

Preface; Organizing Committees; CONTENTS; Variable Selection and Post-Estimation of Regression Parameters Using Quasi-Likelihood Approach S. Fallahpour and S. E. Ahmed; 1. Introduction; 2. Improved Estimation and Variable Selection Strategies; 2.1. Pretest Estimations; 2.2. Absolute Penalty Estimator (APE); 3. Asymptotic Distribution Bias and Risk; 3.1. Bias and Risk Comparison; 4. Monte Carlo Simulation; 5. Conclusions; Acknowledgments; 6. Proofs; References; Maximum Likelihood Estimates for Markov-Additive Processes of Arrivals by Aggregated Data A. M. Andronov; 1. Introduction

2. Markov-Additive Process of arrivals3. Problem of parameter estimation; 4. Derivatives of the expectation; 4.1. Derivatives of with respect to; 4.2. Derivatives of with respect to; 5. Derivatives of the covariance matrix C; 5.1. Derivatives of C with respect to; 5.2. Derivatives of C with respect to; 6. Score function; 7. Parameter identification; 8. Numerical example; 9. Conclusions; Acknowledgement; References; A Simple and Efficient Method of Estimation of the Parameters of a Bivariate Birnbaum-Saunders Distribution Based on Type-II Censored Samples N. Balakrishnan and X. Zhu

1. Introduction2. Bivariate Birnbaum-Saunders Distribution and Some Properties; 3. Form of Data; 4. Estimation Based on Type-II Censored Samples; 5. Simulation Study; 6. Illustrative Data Analysis; 7. Concluding Remarks; References; Analysis of Contingent Valuation Data with Self-Selected Rounded WTP-Intervals Collected by Two-Steps Sampling Plans Yu. K. Belyaev and B. Kristrom; Introduction; Basic assumptions; Sampling design; Statistical model and corresponding log likelihood; Numerical experiment; Conclusion; Acknowledgements; References

Optimal Classification of Multivariate GRF Observations K. Ducinskas and L. Dreiziene1. Introduction; 2. The main concepts and definitions; 3. The asymptotic approximation of ER; 4. Example and discussions; References; Multivariate Exponential Dispersion Models B. Jorgensen and J. R. Martınez; 1. Introduction; 2. Multivariate dispersion models; 2.1. Multivariate proper dispersion models; 2.2. Ordinary exponential dispersion models; 3. Convolution method, additive form; 3.1. The bivariate case; 3.2. The trivariate and multivariate cases; 4. Multivariate discrete exponential dispersion models

4.1. General4.2. A multivariate Poisson model; 4.3. A multivariate binomial model; 4.4. A multivariate negative binomial model; 5. Convolution method, reproductive form; 5.1. General; 5.2. A multivariate gamma model; 6. Multivariate Tweedie models; 6.1. The univariate case; 6.2. The multivariate construction; 7. Multivariate generalized linear models; 8. Discussion; Acknowledgements; References; Statistical Inference with the Limited Expected Value Function M. Kaarik and H. Kadarik; 1. Limited expected value function; 1.1. Definiton and properties; 1.2. Premium calculation

2. Distribution fitting

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed February 5, 2014).

ISBN:

9789814449403

9814449407

OCLC:

844311121