LINEAR MODELS
An Integrated Approach

by Debasis Sengupta (Indian Statistical Institute, Kolkata)
& S Rao Jammalamadaka (University of California, Santa Barbara)

Linear Models: An Integrated Approach aims to provide a clearer as well as a deeper unstanding of the general linear model using simple statistical f the general linear model using simple statistical ideas. Elegant geometric arguments are also invoked as needed and a review of vector spaces and matrices is provided to make the treatment self-contained. Complex, matrix-algebraic methods, such as those used in the rank-deficient case, are replaced by statistical proofs that are more transparent and that show the parallels with the simple linear model.

This book has the following special features:

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Use of simple statistical ideas such as linear zero functions and covariance adjustment to explain the fundamental as well as advanced concepts
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Emphasis on statistical interpretation of complex algebraic results
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A thorough treatment of the singular linear model, including the case of multivariate response
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A unified discussion of models with a partially unknown dispersion matrix, including mixed-effects / variance components models and models for spatial and time series data
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Insight into updates in the linear model and their connection with diagnostics, design, variable selection, the Kalman filter, etc.
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An extensive discussion of the foundations of linear inference, along with linear alternatives to least squares
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Coverage of other special topics such as collinearity, stochastic and inequality constraints, misspecified models, etc.
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Simpler proofs of numerous known results
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Pointers to current research through examples and exercises


Contents:



Readership: Researchers, lecturers and postgraduates in statistics and applied mathematics.

644pp Pub. date: Mar 2003
ISBN 981-02-4592-0 US$82 / £60

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