The detection of residual serial correlation in linear mixed models.
Diggle (1988) described how the empirical semi-variogram of ordinary least squares residuals can be used to suggest an appropriate serial correlation structure in stationary linear mixed models. In this paper, this approach is extended to non-stationary models which include random effects other than intercepts, and will be applied to prostate cancer data, taken from the Baltimore Longitudinal Study of Aging. A simulation study demonstrates the effectiveness of this extended variogram for improving the covariance structure of the linear mixed model used to describe the prostate data.
Verbeke
G
G
Biostatistical Centre for Clinical Trials, Catholic University of Leuven, Belgium.
Lesaffre
E
E
Brant
L J
LJ
D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D017430:Prostate-Specific Antigen / Q000097:blood; D011470:Prostatic Hyperplasia / Q000453:epidemiology; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D012984:Software; D013997:Time Factors
D017430:Prostate-Specific Antigen