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Linear Models And Generalizations: Least Squares And Alternatives.

Linear Models And Generalizations: Least Squares And Alternatives.

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  • ISBN:9783540742265
  • Editorial: Springer
  • Fecha de la edición:
  • Encuadernación:Tapa dura
  • Dimensiones: 0 cm x 0 cm
  • Nº Pág.:570
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This book provides an up-to-date account of the theory and applications of linear models. 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. 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.
For this third edition the text has been extensively revised and contains the latest developments in the area of linear models. Many new topics like regression techniques, nonparametric regression, bagging, boosting, regression trees and full likelihood methods for correlated response in categorical data have been included.

Introduction 1
2 The Simple Linear Regression Model 7
3 The Multiple Linear Regression Model and Its Extensions 33
4 The Generalized Linear Regression Model 143
5 Exact and Stochastic Linear Restrictions 223
6 Prediction in the Generalized Regression Model 271
7 Sensitivity Analysis 321
8 Analysis of Incomplete Data Sets 357
9 Robust Regression 393
10 Models for Categorical Response Variables 411
A Matrix Algebra 489
B Tables 527
C Software for Linear Regression Models 531
References 539
Index

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