Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models,two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus isapplied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis ofvariance, analysis of covariance, and linear mixed models. Recent advances inthe methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed.
- ISBN: 978-0-471-75498-5
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 672
- Fecha Publicación: 01/02/2008
- Nº Volúmenes: 1
- Idioma: Inglés