
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 selfcontained. Complex, matrixalgebraic
methods, such as those used in the rankdeficient 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:

Use
of simple statistical ideas such as linear zero functions and covariance
adjustment to explain the fundamental as well as advanced concepts 

Emphasis
on statistical interpretation of complex algebraic results 

A
thorough treatment of the singular linear model, including the case
of multivariate response 

A
unified discussion of models with a partially unknown dispersion
matrix, including mixedeffects / variance components models and
models for spatial and time series data 

Insight
into updates in the linear model and their connection with diagnostics,
design, variable selection, the Kalman filter, etc. 

An
extensive discussion of the foundations of linear inference, along
with linear alternatives to least squares 

Coverage
of other special topics such as collinearity, stochastic and inequality
constraints, misspecified models, etc. 

Simpler
proofs of numerous known results 

Pointers
to current research through examples and exercises 
Contents:
 Review of Linear Algebra
 Review of Statistical Results
 Estimation in the Linear Model
 Further Inference in the Linear Model
 Analysis of Variance in Basic Designs
 General Linear Model
 Misspecified or Unknown Dispersion
 Updates in the General Linear Model
 Multivariate Linear Model
 Linear Inference ?Other Perspectives
Readership: Researchers, lecturers and postgraduates in statistics
and applied mathematics.
644pp 
Pub. date: Mar 2003 
ISBN 9810245920 
US$68 / £46 
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