Generalized Linear Models with Random Effects: Unified Analysis via H-likelihoodCRC Press, 2006. 7. 13. - 416페이지 Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis. |
목차
1 | |
5 | |
Generalized Linear Models | 37 |
Quasilikelihood | 65 |
Extended Likelihood Inferences | 97 |
Normal linear mixed models | 135 |
Hierarchical GLMs | 173 |
HGLMs with structured dispersion | 203 |
Smoothing | 267 |
Randomeffect models for survival data | 293 |
Double HGLMs | 319 |
Further topics | 343 |
363 | |
380 | |
381 | |
385 | |
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adjusted profile algorithm allow analysis applied approach approximation assumed assumption called canonical Chapter components compute conditional consider constant correlation corresponding covariates defined density depends derived deviance DHGLM dispersion parameters distribution equations equivalent error estimate example expected extended extended likelihood factors Figure fixed fixed parameters follows formula frailty function gamma given gives hazard HGLM independent indicate inferences interest interval joint known leads likelihood linear linear model loglihood marginal matrix maximization mean measurements method missing mixed model Nelder normal Note observed obtained patient plots points Poisson probability problem procedure profile likelihood provides random effects regression residuals respectively response sample scale shows smoothing standard statistical structure Suppose Table term transformation unknown values variables variance vector weight
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375 페이지 - LM (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346: 1937-1947.