An advanced discussion of linear models with mixed or random
effects.
In recent years a breakthrough has occurred in our ability to
draw inferences from exact and optimum tests of variance component
models, generating much research activity that relies on linear
models with mixed and random effects. This volume covers the most
important research of the past decade as well as the latest
developments in hypothesis testing. It compiles all currently
available results in the area of exact and optimum tests for
variance component models and offers the only comprehensive
treatment for these models at an advanced level.
Statistical Tests for Mixed Linear Models:
* Combines analysis and testing in one self-contained
volume.
* Describes analysis of variance (ANOVA) procedures in balanced
and unbalanced data situations.
* Examines methods for determining the effect of imbalance on
data analysis.
* Explains exact and optimum tests and methods for their
derivation.
* Summarizes test procedures for multivariate mixed and random
models.
* Enables novice readers to skip the derivations and discussions
on optimum tests. Offers plentiful examples and exercises, many
of which are numerical in flavor.
* Provides solutions to selected exercises.
Statistical Tests for Mixed Linear Models is an accessible
reference for researchers in analysis of variance, experimental
design, variance component analysis, and linear mixed models. It is
also an important text for graduate students interested in mixed
models.
Tabla de materias
Nature of Exact and Optimum Tests in Mixed Linear Models.
Balanced Random and Mixed Models.
Measures of Data Imbalance.
Unbalanced One-Way and Two-Way Random Models.
Random Models with Unequal Cell Frequencies in the Last
Stage.
Tests in Unbalanced Mixed Models.
Recovery of Inter-Block Information.
Split-Plot Designs Under Mixed and Random Models.
Tests Using Generalized P-Values.
Multivariate Mixed and Random Models.
Appendix.
General Bibliography.
Indexes.
Sobre el autor
Andre I. Khuri, Ph D, is Professor of Statistics at the
University of Florida in Gainesville and author of Advanced
Calculus with Applications in Statistics and Response
Surfaces: Designs and Analyses.
Thomas Mathew and Bimal K. Sinha are Professors of
Statistics at the University of Maryland. Professor Sinha is
coauthor of Robustness of Statistical Tests.
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