ePub Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) download

by Ludwig Fahrmeir,Gerhard Tutz,W. Hennevogl

Author:

Ludwig Fahrmeir,Gerhard Tutz,W. Hennevogl

ISBN13:

978-1441929006

ISBN:

1441929002

Language:

English

Publisher:

Springer; Softcover reprint of hardcover 2nd ed. 2001 edition (December 1, 2010)

Category:

Science

Subcategory:

Biological Sciences

ePub file:

1541 kb

Fb2 file:

1496 kb

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Rating:

4.6

Votes:

546

Ludwig Fahrmeir Gerhard Tutz W. Hennevogl Biological Sciences English

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Springer Series in Statistics. Authors: Fahrmeir, Ludwig, Tutz, Gerhard.

Springer Series in Statistics. Multivariate Statistical Modelling Based on Generalized Linear Models. This book does not have a competitor for analyzing multivariate data with generalized linear models. The authors obviously put a great deal of work into this boo. .There are nearly 40 example. rawn from a variety of fields, extensively worked, and then reworked in succeeding chapters. I conclude by endorsing this book whole-heartedly.

from book Springer Series in Statistics. We focus on generalized linear models and present L 1-penalty approaches for factor selection and clustering of categories. Multivariate statistical modelling based on generalized linear models, Ludwig Fahrmeir, Gerhard Tutz. Article · January 1994 with 1,340 Reads. How we measure 'reads'. Classical statistical models for regression, time series and longitudinal data analysis are generally useful in situations where data are approximately Gaussian and can be explained by some linear structure. These models are easy to interpret and the methods are theoretically well understood and investigated.

Stationary Time Series. Ludwig Fahrmeir Gerhard Tutz Wolfgang Hennevogl. Preface ' v List of Examples xv List of Figures xix List of Tables xxiii 1 Introduction 1 . Outline and examples . Fahrmeir/1" utz: Multivariate Statistical Modelling Based on Generalized Linear Models. Farrell: Multivariate Calculation. Federer: Statistical Design and Analysis for lntercropping Experiments.

Fahrmeir and Tutz have given the statistics community a wonderful .

Fahrmeir and Tutz have given the statistics community a wonderful resource for both teaching and reference. Rick Chappell, Journal of the American Statistical Association, Vol. 98 (463), 2003).

Classical statistical models for regression, time series and longitudinal . Ludwig Fahrmeir, Gerhard Tutz

Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introduction of generalized linear models (GLM) a much more flexible instrument for sta tistical modelling has been created. The broad class of GLM's includes some of the classicallinear models as special cases but is particularly suited for categorical discrete or nonnegative responses. Ludwig Fahrmeir, Gerhard Tutz. Springer Science & Business Media, 11 нояб.

by Ludwig Fahrmeir & Gerhard Tutz. and statistics quickly. It brings together many of the main ideas in modern statistics in one plac. Based on the successful National Academies Summer Institute for Undergraduate Biology Education. 07 MB·859 Downloads·New!

Ludwig Fahrmeir, Gerhard Tutz.

Ludwig Fahrmeir, Gerhard Tutz. Springer Science & Business Media, Mar 14, 2013 - Mathematics - 518 pages.

Автор: Fahrmeir Ludwig, Tutz Gerhard, Hennevogl W. Название: Multivariate Statistical Modelling .

The book illustrates the development of linear statistical models with applications to a variety of fields including mathematics, statistics, biostatistics, engineering, and the physical sciences.

Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics). Download (djvu, . 2 Mb) Donate Read.

Start by marking Multivariate Statistical Modelling Based on Generalized Linear .

Start by marking Multivariate Statistical Modelling Based on Generalized Linear Models as Want to Read: Want to Read savin. ant to Read. This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences.

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

Halloween

Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.

The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.

Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.

Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).

Thordira

Great book! Presents clear information about statistical computational details, as well as a number of nonstandard models (including those of Tutz's original work). The book has a transparent build-up, from more easy modeling exercises to advanced applications. I like best the part on generalized linear time series modeling, using the extended Kalman filter in the context of the EM algorithm. The only critique I have concerns the handling of (the variance of) the measurement error term in the associated generalized state space model (this measurement error should be modeled as a constrained martingale difference).

Mojind

[1] Studying bioinformatics? You must be familiar with multivariate analysis. This book is absolutely an important reference.

[2] A researcher of statistical pattern recognition? Without doubt, you need this up-to-date book to stuff your toolbox.

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