Fitting semiparametric random effects models to large data sets.

Published

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

Pubmed Central 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