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 a ISBN This book at Amazon print version This book at Amazon electronic version. Back to search.
giuliettasprint.konfer.eu: Statistical Tests for Mixed Linear Models (Wiley Series in Probability and Statistics) (): André I. Khuri, Thomas Mathew, Bimal K. Statistical Tests for Mixed Linear Models: Combines analysis and testing in one self-contained volume. Describes analysis of variance (ANOVA) procedures in.
Record created , last modified Similar records. In recent years, analysts have made significant breakthroughs regarding statistical tests for such models. Traditional, analysis of variance ANOVA models are well developed for fixed effects models, which are those in which the researcher has complete control over assignment of factors and factor levels. For models with random effects as often exists in observational studies, where for example, subject educational level varies but is not under the control of the researcher , too, ANOVA models have long existed.
Models with both types of effects, however, present some special challenges, and Statistical Tests for Mixed Linear Models lays out appropriate solutions. Covering both balanced those with equal numbers of observations in all subclasses and unbalanced models those with at least one subclass with a different number of observations compared to the others , Statistical Tests for Mixed Linear Models presents derivations of both exact and optimal tests for variance component models, as well as guidance on using such tests for hypothesis testing.
While little attention is paid to conducting such tests with commonly available statistical software e. In several places, conceptual algorithms are given to allow the reader to conduct tests not offered in standard software.
Designed primarily as a course textbook, Statistical Tests for Mixed Linear Models includes student exercises at the end of each chapter, an appendix that gives the solutions to selected problems, and an ample bibliography. Beyond formal use in the classroom, the book also may serve as a reference guide for researchers beyond their student years who wish to know more about exact or optimal tests for mixed linear models.
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Interested readers, however, should be aware that this is not an introductory text on experimental design or ANOVA. To make best use of Statistical Tests for Mixed Linear Models , readers should be well versed in both.