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Book synopsis

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

Book details

Book category Books in English Mathematics & science Mathematics Algebra

• Full title: Linear Models and the Relevant Distributions and Matrix Algebra
• Author: HARVILLE
• Language: English
• Binding: Hardback
• Number of pages: 524
• EAN: 9781138578333
• ISBN: 9781138578333
• ID: 18352629
• Publisher: Taylor & Francis Ltd
• Weight: 1142 g
• Dimensions: 186 × 261 × 36 mm
• Date of publishing: 13. March 2018

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