Linear Models and the Relevant Distributions and Matrix Algebra
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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
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