| 书目名称 | Models for Discrete Longitudinal Data |
| 编辑 | Geert Molenberghs,Geert Verbeke |
| 视频video | |
| 概述 | The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association‘s Excellence in Continuing Education Award, based on |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention...The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. |
| 出版日期 | Book 2005 |
| 关键词 | Excel; Fitting; Generalized linear model; Likelihood; SAS; best fit; correlation; statistics |
| 版次 | 1 |
| doi | https://doi.org/10.1007/0-387-28980-1 |
| isbn_softcover | 978-1-4419-2043-0 |
| isbn_ebook | 978-0-387-28980-9Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2005 |
发表于 2025-3-21 19:03:08
