Linear mixed models with flexible distributions of random effects for longitudinal data.
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.
Duke Scholars
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Related Subject Headings
- Statistics & Probability
- Longitudinal Studies
- Linear Models
- Likelihood Functions
- Humans
- Data Interpretation, Statistical
- Cholesterol
- Biometry
- 4905 Statistics
- 0199 Other Mathematical Sciences
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Statistics & Probability
- Longitudinal Studies
- Linear Models
- Likelihood Functions
- Humans
- Data Interpretation, Statistical
- Cholesterol
- Biometry
- 4905 Statistics
- 0199 Other Mathematical Sciences