Posted by **kalyan1232008** at March 26, 2009

CRC; 1 edition | May 25, 2000 | English | ISBN-10: 0824790340 | 423 pages | 6.0 MB

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Posted by **step778** at Nov. 2, 2016

1993 | pages: 194 | ISBN: 0412300400 | PDF | 4,1 mb

Posted by **advisors** at Jan. 8, 2015

1994 | 184 Pages | ISBN: 0412300400 | PDF | 4 MB

Posted by **tukotikko** at May 25, 2014

1994 | 184 Pages | ISBN: 0412300400 | PDF | 4 MB

Posted by **advisors** at Jan. 11, 2014

1994 | 184 Pages | ISBN: 0412300400 | PDF | 4 MB

Posted by **ksveta6** at Nov. 21, 2015

2015 | ISBN: 1118730038 | English | 472 pages | PDF + EPUB | 7 MB + 14 MB

Posted by **arundhati** at July 27, 2015

2010 | ISBN-10: 1420091557 | 316 pages | PDF | 2 MB

Posted by **fdts** at March 31, 2015

by L. Fahrmeir

English | 1994 | ISBN: 0387942335 | 425 pages | DJVU | 3.36 MB

Posted by **fdts** at Dec. 25, 2014

by Raymond H. Myers, Douglas C. Montgomery

English | 2010 | ISBN: 0470454636 | 496 pages | PDF | 28.11 MB

Posted by **Alexpal** at Sept. 19, 2006

Springer | ISBN 0387982183 | 1997 Year | PDF | 1,09 Mb | 276 Pages

Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.