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＜電子ブック＞
Linear Models and Generalizations : Least Squares and Alternatives / by C. Radhakrishna Rao, Helge Toutenburg, Shalabh, Christian Heumann
(Springer Series in Statistics. ISSN:2197568X)

版	3rd ed. 2008.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2008
本文言語	英語
大きさ	XIX, 572 p : online resource
著者標目	*Rao, C. Radhakrishna author Toutenburg, Helge author Shalabh author Heumann, Christian author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Statistics  LCSH:Econometrics LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical statistics LCSH:Operations research FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Quantitative Economics FREE:Probability and Statistics in Computer Science FREE:Operations Research and Decision Theory
一般注記	The Simple Linear Regression Model -- The Multiple Linear Regression Model and Its Extensions -- The Generalized Linear Regression Model -- Exact and Stochastic Linear Restrictions -- Prediction in the Generalized Regression Model -- Sensitivity Analysis -- Analysis of Incomplete Data Sets -- Robust Regression -- Models for Categorical Response Variables Thebookisbasedonseveralyearsofexperienceofbothauthorsinteaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and o?ers a selectionofclassicalandmodernalgebraicresultsthatareusefulinresearch work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss fu- tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and a variety of practical applications will be useful not only to students but also to researchers and consultants in statistics HTTP:URL=https://doi.org/10.1007/978-3-540-74227-2

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