Multivariate Statistical Modelling Based on Generalized Linear Models

Ludwig Fahrmeir, Gerhard Tutz - Collection Springer Series In Statistics

517 pages, parution le 01/05/2001 (2^eme édition)

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Résumé

The first edition of Multivariate Statistical Modelling provided an extension of classical models for regression, time series, and longitudinal data to a much broader class including categorical data and smoothing concepts. Generalized linear modesl for univariate and multivariate analysis build the central concept, which for the modelling of complex data is widened to much more general modelling approaches. The primary aim of the new edition is to bring the book up-to-date and to reflect the major new developments over the past years.

The authors give a detailed introductory survey of the subject based on the alaysis of real data drawn from a variety of subjects, including the biological sciences, economics, and the social sciences. Technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. The appendix serves as a reference or brief tutorial for the concepts of EM algorithm, numberical integration, MCMC and others.

The topics covered inlude:

Models for multi-categorial responses,
model checking,
semi- and nonparametric modelling,
time series and longitudinal data,
random effects models,
state-space models
survival analysis

In the new edition Bayesian concepts which are of growing importance in statistics are treated more extensively. The chapter on nonparametric and semiparametric generalized regression has been rewritten totally, random effects models now cover nonparametric maximum likelihood and fully Bayesian approaches, and state-space and hidden Markov models have been supplemented with an extension to models that can accommodate for spatial and spatiotemporal data. The authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, this book is ideally suited for applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from econometrics, biometrics and the social sciences.

Written for:
Researchers, graduate students

Sommaire

Introduction
Modelling and Analysis of Cross-sectional Data: A Review of Univariate Generalized Linear Models
Models for Multicategorical Responses: Multivariate Extensions of Generalized Linear Models
Selecting and Checking Models
Semi- and Nonparametric Approaches to Regression Analysis
Fixed Parameter Models for Time Series and Longitual Data
Random Effects Models
State Space and Hidden Markov Models
Survival Models

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Ludwig Fahrmeir, Gerhard Tutz
Collection	Springer Series In Statistics
Parution	01/05/2001
Édition	2^eme édition
Nb. de pages	517
Format	16 x 24
Couverture	Relié
Poids	883g
Intérieur	Noir et Blanc
EAN13	9780387951874
ISBN13	978-0-387-95187-4