𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

✍ Scribed by Jianhua Z. Huang; Min Chen; Mehdi Maadooliat; Mohsen Pourahmadi


Book ID
113757563
Publisher
Elsevier Science
Year
2012
Tongue
English
Weight
319 KB
Volume
142
Category
Article
ISSN
0378-3758

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Mixed effect models of longitudinal Alzh
✍ J. Kevin Milliken; Steven D. Edland πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons 🌐 English βš– 367 KB πŸ‘ 2 views

Longitudinal studies of cognitive function in Alzheimer's disease (AD) patients are powerful tools to better understand the biology and natural history of the disease, but the attributes of the studies that make them valuable also pose special challenges to analysts. A fundamental problem is the acc

Residuals analysis of the generalized li
✍ Yue-Cune Chang πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons 🌐 English βš– 196 KB πŸ‘ 3 views

The generalized estimation equation (GEE) method, one of the generalized linear models for longitudinal data, has been used widely in medical research. However, the related sensitivity analysis problem has not been explored intensively. One of the possible reasons for this was due to the correlated

Summarizing the goodness of fit of gener
✍ Beiyao Zheng πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons 🌐 English βš– 103 KB πŸ‘ 3 views

This paper extends four goodness-of-ΓΏt measures of a generalized linear model (GLM) to random e ects and marginal models for longitudinal data. The four measures are the proportional reduction in entropy measure, the proportional reduction in deviance measure, the concordance correlation coe cient a

On Joint Estimation of Regression and Ov
✍ Brajendra C. Sutradhar; R.Prabhakar Rao πŸ“‚ Article πŸ“… 1996 πŸ› Elsevier Science 🌐 English βš– 646 KB

Liang and Zeger introduced a class of estimating equations that gives consistent estimates of regression parameters and of their variances in the class of generalized linear models for longitudinal data. When the response variable in such models is subject to overdispersion, the oerdispersion parame