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eBook details

• Title: Linear Models and Regression with R
• Author : Debasis Sengupta & Sreenivasa Rao Jammalamadaka
• Release Date : January 30, 2019
• Genre: Mathematics,Books,Science & Nature,
• Pages : * pages
• Size : 29398 KB

Description

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed.

The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.

This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:
Contents: IntroductionRegression and the Normal DistributionEstimation in the Linear ModelFurther Inference in the Linear ModelModel Building and Diagnostics in RegressionAnalysis of VarianceGeneral Linear ModelMisspecified or Unknown DispersionUpdates in the General Linear ModelMultivariate Linear ModelLinear Inference — Other Perspectives
Readership: Researchers, lecturers, postgraduates, graduates and undergraduates in statistics and applied mathematics.Linear Models;Regression;Analysis of Variance;Singular Design and Dispersion Matrices;Diagnostics;Model-Building;Multivariate Response;Linear Inference0Key Features:Complete coverage of topics for a course on regression including data sets and implementation in RSpecial topics covered include collinearity, misspecified models, Kalman filter, multivariate response, foundations of linear inferenceComprehensive theory with latest literature and references and thorough treatment of the singular linear model

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