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PARK, TAESUNG 한국외국어대학교 외국학종합연구센터 부설 기초과학연구소 1993 기초과학연구 Vol.3 No.-
Liang and Zeger (1986) proposed a generalized estimating equations approach to the analysis of longitudinal data. Their analyses have assumed that missing observations are missing completely at ransom in the sense of Rubin (1976). However, when this assumption does not hold, their analyses may result in biased results. In this paper, we develop a simple and practical test procedure for testing this assumption. The proposed test is an extension of the test of Little (1988).
PARK, TAESUNG 한국외국어대학교 외국학종합연구센터 부설 기초과학연구소 1993 기초과학연구 Vol.3 No.-
The seemingly unrelated regression model is viewed in the context of repeated measures analysis. Regression parametersand the variance-covariance matrix of the seemingly unrelated regression model can be estimated by using two-stage Aitken estimation. The first stage is to obtain a consistent estimator of the variance-covariance matrix. The second stage uses this matrix to obtain the generalized least squares estimators of the regression parameters. The maximum likelihood (ML) estimators of the regression parameters can be obtained by performing the two-stage estimation iteratively. The iterative two-stage estimation procedure is shown to be equivalent to the EM algorithm (Dempster, Laird, and Rubin, 1977) proposed by Jennrich and Schluchter (1986) and Laird, Lange, and Stram (1987) for repeated measures data. The equivalence of the iterative two-stage estimator and the ML estimator has been previously demonstrated empirically in a Monte Carlo study by Kmenta and Gilbert (1968). It does not appear to be widely known that the two estimators are equivalent theoretically. This paper demonstrates this equivalence.