Sahai / Ojeda Analysis of Variance for Random Models, Volume 2: Unbalanced Data
Erscheinungsjahr 2007
ISBN: 978-0-8176-4425-3
Verlag: Birkhäuser Boston
Format: PDF
Kopierschutz: 1 - PDF Watermark
Theory, Methods, Applications, and Data Analysis
E-Book, Englisch, 480 Seiten, Web PDF
Reihe: Mathematics and Statistics
ISBN: 978-0-8176-4425-3
Verlag: Birkhäuser Boston
Format: PDF
Kopierschutz: 1 - PDF Watermark
Analysis of variance (ANOVA) models have become widely used tools and play a fundamental role in much of the application of statistics today. In particular, ANOVA models involving random effects have found widespread application to experimental design in a variety of fields requiring measurements of variance, including agriculture, biology, animal breeding, applied genetics, econometrics, quality control, medicine, engineering, and social sciences.
This two-volume work is a comprehensive presentation of different methods and techniques for point estimation, interval estimation, and tests of hypotheses for linear models involving random effects. Both Bayesian and repeated sampling procedures are considered. Volume 1 examines models with balanced data (orthogonal models); Volume 2 studies models with unbalanced data (nonorthogonal models).
Accessible to readers with only a modest mathematical and statistical background, the work will appeal to a broad audience of students, researchers, and practitioners in the mathematical, life, social, and engineering sciences. It may be used as a textbook in upper-level undergraduate and graduate courses, or as a reference for readers interested in the use of random effects models for data analysis.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Matrix Preliminaries and General Linear Model.- Some General Methods for Making Inferences about Variance Components.- One-Way Classification.- Two-Way Crossed Classification without Interaction.- Two-Way Crossed Classification with Interaction.- Three-Way and Higher-Order Crossed Classifications.- Two-Way Nested Classification.- Three-Way Nested Classification.- General r-Way Nested Classification.
