ENGLISH

The Linear Model and Hypothesis: A General Unifying Theory

By George Seber (auth.)

Book information

Publisher
Springer International Publishing
Year
2015
ISBN
978-3-319-21929-5, 978-3-319-21930-1
DOI
10.1007/978-3-319-21930-1
Language
english
Format
PDF
Filesize
2 MB (1710116 bytes)
Series
Springer Series in Statistics
Edition
1
Pages
IX, 205\208
Time added
2015-11-25 16:51:03
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Description

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 involvematrix 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 to other models in the analysis of variance, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.

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