Multivariate Statistical Modelling Based on Generalized Linear ModelsSpringer Science & Business Media, 14.03.2013 - 518 Seiten 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. |
Inhalt
A Review | 15 |
Linear Models | 29 |
Multivariate | 68 |
Selecting and Checking Models | 139 |
Semi and Nonparametric Approaches | 173 |
Fixed Parameter Models for Time Series | 241 |
Random Effects Models 283 | 282 |
GLMS | 305 |
State Space and Hidden Markov Models | 331 |
Survival Models 385 | 384 |
A | 433 |
B Software for Fitting Generalized Linear Models | 454 |
| 467 | |
| 505 | |
| 512 | |
Andere Ausgaben - Alle anzeigen
Multivariate Statistical Modelling Based on Generalized Linear Models Ludwig Fahrmeir,Gerhard Tutz Keine Leseprobe verfügbar - 2010 |
Multivariate Statistical Modelling Based on Generalized Linear Models Ludwig Fahrmeir,Gerhard Tutz Keine Leseprobe verfügbar - 2001 |
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additive algorithm analysis approach approximation assumed assumption autoregressive B-splines basis functions Bayesian Bayesian inference binary responses binomial Chapter components computed conditional considered correlated corresponding covariance matrix covariate effects cumulative model denotes density design matrix deviance EM algorithm Example exponential family Fahrmeir Fisher scoring Gauss-Hermite Gaussian Gibbs sampling given GLMs H₁ hazard function infection iterations Kalman filter kernel likelihood estimation linear models linear predictor link function log-likelihood log-linear logit model longitudinal data marginal models Markov maximization MCMC methods multicategorical multinomial multivariate nonlinear nonparametric observations obtained p-values penalized polynomial posterior mean posterior mode prior probability quasi-likelihood random effects models regression models residuals response function response variable sample score function Section semiparametric smoother space models specified statistic Table threshold Tutz univariate values vaso constriction vector weights Zeger βο με(β σ²