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Linear Models and Generalizations [electronic resource] :Least Squares and Alternatives / by C. Radhakrishna Rao, Shalabh, Helge Toutenburg, Christian Heumann.

by Rao, C. Radhakrishna [author.]; Shalabh [author.]; Toutenburg, Helge [author.]; Heumann, Christian [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Series in Statistics: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Edition: Third Extended Edition.Description: online resource.ISBN: 9783540742272.Subject(s): Statistics | Computer science | Distribution (Probability theory) | Mathematical statistics | Economics, Mathematical | Statistics | Statistical Theory and Methods | Game Theory/Mathematical Methods | Probability Theory and Stochastic Processes | Probability and Statistics in Computer Science | Operations Research/Decision TheoryDDC classification: 519.5 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Some of the highlights include: sensitivity analysis and model selection, analysis of incomplete data, analysis of categorical data based on a unified presentation of generalized linear models including GEE- and full likelihood methods for correlated response, an extensive appendix on matrix theory, useful to researchers in econometrics, engineering and optimization theory. For this third edition the text has been extensively revised and contains the latest developments in the area of linear models.
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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.

This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Some of the highlights include: sensitivity analysis and model selection, analysis of incomplete data, analysis of categorical data based on a unified presentation of generalized linear models including GEE- and full likelihood methods for correlated response, an extensive appendix on matrix theory, useful to researchers in econometrics, engineering and optimization theory. For this third edition the text has been extensively revised and contains the latest developments in the area of linear models.

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