Linear mixed models with flexible distributions of random effects for longitudinal data.

Published

Journal Article

Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among-individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics 55, 363-390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user-chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.

Full Text

Duke Authors

Cited Authors

  • Zhang, D; Davidian, M

Published Date

  • September 2001

Published In

Volume / Issue

  • 57 / 3

Start / End Page

  • 795 - 802

PubMed ID

  • 11550930

Pubmed Central ID

  • 11550930

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.0006-341x.2001.00795.x

Language

  • eng