Statistical Inference for Models with Multivariate t-Distributed Errors - Tapa dura

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A. K. Md. Ehsanes Saleh, PhD, is Professor Emeritus and Distinguished Research Professor in the School of Mathematics and Statistics at Carleton University, Canada. He has published well-over 200 journal articles, and his research interests include nonparametric statistics, order statistics, and robust estimation.  Dr. Saleh is a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and the Bangladesh Academy of Sciences.

M. Arashi, PhD, is Associate Professor in the Department of Statistics at Shahrood University of Technology, Iran.  The recipient of the Award for Teaching Excellence from Shahrood University in 2013, his research interests include shrinkage estimation, distribution theory, and multivariate analysis. 

S. M. M. Tabatabaey, PhD, is Associate Professor in the Department of Statistics at Ferdowski University of Mashhad, Iran. The author of over fifteen journal articles, he is also a member of the Institute of Mathematical Statistics and the Iranian Statistical Society.

De la contraportada

Uniquely presents systematic analytical results using Student’s t-distributed errors in linear models

Statistical Inference for Models with Multivariate t-Distributed Errors presents a wide array of applications for the analysis of multivariate observations and emphasizes the Student’s t-distribution method. The book illustrates the development of linear statistical models with applications to a variety of fields including mathematics, statistics, biostatistics, engineering, and the physical sciences.

The book begins with a summary of the results under normal theory and proceeds to the statistical analysis of location models, simple regression, analysis of variance (ANOVA), parallelism, multiple regression, ridge regression, multivariate and simple multivariate linear models, and linear prediction. Providing a clear and balanced introduction to statistical inference, the bookalso features:

• A unique connection to normal distribution, Bayesian analysis, prediction problems, and Stein shrinkage estimation
• Practical real-world examples that address linear regression models with non-normal errors with practical real-world examples
• Plentiful applications and end-of-chapter problems that enhance the applications for the analysis of multivariate observations
• An up-to-date bibliography featuring the latest trends and advances to provide a collective resource for research

Statistical Inference for Models with Multivariate t-Distributed Errors is an excellent upper-undergraduate and graduate-level textbook for courses in multivariate analysis, regression, linear models, and Bayesian analysis. The book is also a useful resource for statistical practitioners who need solid methodology within mathematical and quantitative statistics.