Abstract:
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Zhang D,Davidian Mdoi
10.1111/j.0006-341x.2001.00795.xsubject
Has Abstractpub_date
2001-09-01 00:00:00pages
795-802issue
3eissn
0006-341Xissn
1541-0420journal_volume
57pub_type
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