Models for Discrete Longitudinal Data (Springer Series in Statistics)

✍ Scribed by Geert Molenberghs, Geert Verbeke

Publisher: Springer
Year: 2005
Tongue: English
Leaves: 706
Series: Springer Series in Statistics
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

📜 SIMILAR VOLUMES

Models for Discrete Longitudinal Data (S

📁 Models for Discrete Longitudinal Data (Springer Series in Statistics)

✍ Geert Molenberghs, Geert Verbeke 📂 Library 📅 2005 🏛 Springer 🌐 English

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text

Modeling Longitudinal Data (Springer Tex

📁 Modeling Longitudinal Data (Springer Texts in Statistics)

✍ Robert E. Weiss 📂 Library 📅 2005 🏛 Springer 🌐 English

Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis

Longitudinal Categorical Data Analysis (

📁 Longitudinal Categorical Data Analysis (Springer Series in Statistics)

✍ Brajendra C Sutradhar 📂 Library 🌐 English

Models for Discrete Longitudinal Data

📁 Models for Discrete Longitudinal Data

✍ Geert Molenberghs, Geert Verbeke 📂 Library 📅 2005 🏛 Springer 🌐 English

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 principle

Models for Discrete Longitudinal Data

📁 Models for Discrete Longitudinal Data

✍ Geert Molenberghs, Geert Verbeke 📂 Library 📅 2005 🏛 Springer 🌐 English

Goodness-of-Fit Statistics for Discrete

📁 Goodness-of-Fit Statistics for Discrete Multivariate Data (Springer Series in Statistics)

✍ Timothy R.C. Read, Noel A.C. Cressie 📂 Library 📅 1988 🏛 Springer 🌐 English