Estimation and prediction in linear mixed models with skew-normal random effects for longitudinal data.
This paper extends the classical linear mixed model by considering a multivariate skew-normal assumption for the distribution of random effects. We present an efficient hybrid ECME-NR algorithm for the computation of maximum-likelihood estimates of parameters. A score test statistic for testing the existence of skewness preference among random effects is developed. The technique for the prediction of future responses under this model is also investigated. The methodology is illustrated through an application to Framingham cholesterol data and a simulation study.
Lin
Tsung I
TI
Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan. tilin@amath.nchu.edu.tw
Lee
Jack C
JC
D000465:Algorithms; D001699:Biometry; D002318:Cardiovascular Diseases / Q000097:blood / Q000209:etiology; D002784:Cholesterol / Q000097:blood; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D015999:Multivariate Analysis; D012307:Risk Factors
D002784:Cholesterol