Harville D. Linear Models and the Relevant Distributions...Matrix Alg 2018

Textbook in PDF format

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical concepts in the context of a relatively complex setting of great practical importance.
Features:
Provides coverage of matrix algebra that is extensive and relatively self-contained and does so in a meaningful context
Provides thorough coverage of the relevant statistical distributions, including spherically and elliptically symmetric distributions
Includes extensive coverage of multiple-comparison procedures (and of simultaneous confidence intervals), including procedures for controlling the k-FWER and the FDR
Provides thorough coverage (complete with detailed and highly accessible proofs) of results on the properties of various linear-model procedures, including those of least squares estimators and those of the F test.
Features the use of real data sets for illustrative purposes
Includes many exercises
Matrix Algebra: A Primer
Random Vectors and Matrices
The General Linear Model
Estimation and Prediction: Classical Approach
Some Relevant Distributions and Their Properties
Confidence Intervals (or Sets) and Tests of Hypotheses