Fitting semiparametric random effects models to large data sets.
Journal Article (Journal Article)
For large data sets, it can be difficult or impossible to fit models with random effects using standard algorithms due to memory limitations or high computational burdens. In addition, it would be advantageous to use the abundant information to relax assumptions, such as normality of random effects. Motivated by data from an epidemiologic study of childhood growth, we propose a 2-stage method for fitting semiparametric random effects models to longitudinal data with many subjects. In the first stage, we use a multivariate clustering method to identify G<
Full Text
Duke Authors
Cited Authors
- Pennell, ML; Dunson, DB
Published Date
- October 2007
Published In
Volume / Issue
- 8 / 4
Start / End Page
- 821 - 834
PubMed ID
- 17429104
Electronic International Standard Serial Number (EISSN)
- 1468-4357
International Standard Serial Number (ISSN)
- 1465-4644
Digital Object Identifier (DOI)
- 10.1093/biostatistics/kxm008
Language
- eng