Linear Models and the Relevant Distributions and Matrix Algebra

By David A Harville

Book Overview

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 contex 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

• Format: Paperback
• Language: English
• ISBN: 0367572036
• ISBN13: 9780367572037
• Release Date: June 2020
• Publisher: CRC Press
• Length: 538 Pages
• Weight: 2.07 lbs.
• Dimensions: 1.2" x 6.9" x 9.9"