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＜電子ブック＞
Multivariate Statistical Modelling Based on Generalized Linear Models / by Ludwig Fahrmeir, Gerhard Tutz
(Springer Series in Statistics. ISSN:2197568X)

版	2nd ed. 2001.
出版者	New York, NY : Springer New York : Imprint: Springer
出版年	2001
本文言語	英語
大きさ	XXVI, 518 p : online resource
冊子体	Multivariate statistical modelling based on generalized linear models / Ludwig Fahrmeir, Gerhard Tutz ; with contributions from Wolfgang Hennevogl
著者標目	*Fahrmeir, Ludwig author Tutz, Gerhard author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Mathematical models LCSH:Statistics  LCSH:Statistics LCSH:Biometry FREE:Probability Theory FREE:Mathematical Modeling and Industrial Mathematics FREE:Statistics FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Biostatistics
一般注記	1. Introduction -- 2. Modelling and Analysis of Cross-Sectional Data: A Review of Univariate Generalized Linear Models -- 3. Models for Multicategorical Responses: Multivariate Extensions of Generalized Linear Models -- 4. Selecting and Checking Models -- 5. Semi- and Nonparametric Approaches to Regression Analysis -- 6. Fixed Parameter Models for Time Series and Longitudinal Data -- 7. Random Effects Models -- 8. State Space and Hidden Markov Models -- 9. Survival Models -- A. -- A.1 Exponential Families and Generalized Linear Models -- A.2 Basic Ideas for Asymptotics -- A.3 EM Algorithm -- A.4 Numerical Integration -- A.5 Monte Carlo Methods -- B. Software for Fitting Generalized Linear Models and Extensions -- Author Index Since our first edition of this book, many developments in statistical mod­ elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv­ ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models Accessibility summary: This PDF is not accessible. It is based on scanned pages and does not support features such as screen reader compatibility or described non-text content (images, graphs etc). However, it likely supports searchable and selectable text based on OCR (Optical Character Recognition). Users with accessibility needs may not be able to use this content effectively. Please contact us at accessibilitysupport@springernature.com if you require assistance or an alternative format Inaccessible, or known limited accessibility No reading system accessibility options actively disabled Publisher contact for further accessibility information: accessibilitysupport@springernature.com HTTP:URL=https://doi.org/10.1007/978-1-4757-3454-6

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