The linear model and hypothesis : a general unifying theory /

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Bibliographic Details
Author / Creator:Seber, G. A. F. (George Arthur Frederick), 1938- author.
Imprint:Cham : Springer, [2015]
©2015
Description:1 online resource.
Language:English
Series:Springer series in statistics
Springer series in statistics.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11096479
Hidden Bibliographic Details
ISBN:9783319219301
3319219308
9783319219295
3319219294
9783319219295
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (EBSCO, viewed October 14, 2015).
Summary:This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involve matrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality in the analysis of variance to other models, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.
Other form:Printed edition: 9783319219295
Standard no.:10.1007/978-3-319-21930-1
Table of Contents:
  • 1. Preliminaries
  • 2. The Linear Hypothesis
  • 3. Estimation
  • 4. Hypothesis Testing
  • 5. Inference Properties
  • 6. Testing Several Hypotheses
  • 7. Enlarging the Model
  • 8. Nonlinear Regression Models
  • 9. Multivariate Models
  • 10. Large Sample Theory: Constraint-Equation Hypotheses
  • 11. Large Sample Theory: Freedom-Equation Hypotheses
  • 12. Multinomial Distribution
  • Appendix
  • Index.