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This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations.
Some of the highlights include: a special emphasis on sensitivity analysis and model selection; a chapter devoted to the analysis of categorical data based on logit, loglinear, and logistic regressions models; a chapter devoted to incomplete data sets; an extensive appendix on matrix theory, useful to researchers in econometrics, engineering, and optimization theory. The material covered will be invaluable not only to graduate students, but also to research workers and consultants in statistics.
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Linear models (Statistics)Edition | Availability |
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1
Linear Models: Least Squares and Alternatives
2013, Springer London, Limited
in English
1489900241 9781489900241
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2
Linear Models: Least Squares and Alternatives
2006, Springer London, Limited
in English
0387227520 9780387227528
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3
Linear Models: Least Squares and Alternatives (Springer Series in Statistics)
January 24, 1997, Springer
in English
0387945628 9780387945620
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Feedback?July 18, 2024 | Edited by MARC Bot | import existing book |
July 30, 2019 | Edited by MARC Bot | associate edition with work OL330197W |
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October 27, 2009 | Created by WorkBot | add works page |