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Linear modelsa mean model approach /

Moser, Barry Kurt.

Linear modelsa mean model approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linear models/ Barry Kurt Moser.
Reminder of title:	a mean model approach /
Author:	Moser, Barry Kurt.
Published:	San Diego :Academic Press,c1996.
Description:	1 online resource (xii, 228 p.)
Subject:	Linear models (Statistics) -
Online resource:	Click here for online access to this book (查閱全文) (EBSCO eBook)
ISBN:	058549214X (electronic bk.)
ISBN:	9780585492148 (electronic bk.)
ISBN:	9780125084659
ISBN:	012508465X
ISBN:	0080510299
ISBN:	9780080510293

Linear modelsa mean model approach /
Moser, Barry Kurt.

Linear modelsa mean model approach /[electronic resource] :Barry Kurt Moser. - San Diego :Academic Press,c1996. - 1 online resource (xii, 228 p.) - Probability and mathematical statistics. - Probability and mathematical statistics..

Includes bibliographical references (p. 221-223) and index.

Linear Algebra and Related Introductory Topics: Elementary Matrix Concepts. Kronecker Products. Random Vectors. Multivariate Normal Distribution: Multivariate Normal Distribution Function. Conditional Distributionsof Multivariate Normal Vectors. Distributions of Certain Quadratic Forms. Distributions of Quadratic Forms: Quadratic Forms of Normal Random Vectors. Independence. t and F Distributions. Bhats Lemma. Complete, Balanced Factorial Experiments: Models That Admit Restrictions (Finite Models). Models That Do Not Admit Restrictions (Infinite Models). Sum of Squares and Covariance Matrix Algorithms. Expected Mean Squares. Algorithm Applications. Least Squares Regression: Ordinary Least SquaresEstimation. Best Linear Unbiased Estimators. ANOVA Table for the Ordinary Least Squares Regression Function. Weighted Least Squares Regression. Lack of Fit Test. Partitioning the Sum of Squares Regression. The Model Y = X(+ E in Complete, BalancedFactorials. Maximum Likelihood Estimation and Related Topics: Maximum Likelihood Estimators (MLEs) of (and (<+>2. Invariance Property, Sufficiency and Completeness. ANOVA Methods for Finding Maximum Likelihood Estimators. The Likelihood Ratio Test for H(= h. Confidence Bands on Linear Combinations of (. Unbalanced Designs and Missing Data: Replication Matrices. Pattern Matrices and Missing Data. Using Replication and Pattern Matrices Together. Balanced Incomplete Block Designs: General Balanced Incomplete Block Design. Analysis of the General Case. Matrix Derivations of Kempthornes Inter- and Intra-Block Treatment Difference Estimators. Less Than Full Rank Models: Model Assumptions and Examples. The Mean Model Solution. Mean Model Analysis When cov(E) = (<+>2I<->n. Estimable Functions. Mean Model Analysis When cov(E) = (<+>2V. The General Mixed Model: The Mixed Model Structure and Assumptions. Random Portion Analysis: Type I Sumof Squares Method. Random Portion Analysis: Restricted Maximum Likelihood Method. Random Portion Analysis: A Numerical Example. Fixed Portion Analysis. Fixed Portion Analysis: A Numerical Example. Appendixes. References. Subject Index.

Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory. Key Features * Provides a versatile format for investigating linear model theory, using the mean model * Uses examples that are familiar to the student: * design of experiments, analysis of variance, regression, and normal distribution theory * Includes a review of relevant linear algebra concepts * Contains fully worked examples which follow the theorem/proof presentation.

ISBN: 058549214X (electronic bk.)

Source: 77293:77293Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

148693
Linear models (Statistics)
Subjects--Index Terms:

Statistical analysisIndex Terms--Genre/Form:

172687
Electronic books.

LC Class. No.: QA279 / .M685 1996eb

Dewey Class. No.: 519.5/35

Linear modelsa mean model approach /
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