On Pseudo-EBLUP Under Some Model for Longitudinal Data with Auxiliary Variables
Abstract
The problem of modeling longitudinal profiles is considered assuming that the
population and elements affiliation to subpopulations may change in time. The considerations are
based on a model with auxiliary variables for longitudinal data with element and subpopulation
specific random components (compare Verbeke, Molenberghs, 2000; Hedeker, Gibbons, 2006)
which is a special case of the General Linear Model (GLM) the General Linear Mixed Model
(GLMM). In the paper the pseudo-empirical best linear unbiased predictor (Pseudo-EBLUP) based
on model-assisted approach will be presented along with its mean squared error (MSE) and its
estimators. In the simulation study its accuracy will be compared with some calibration estimators
which are based on model-assisted approach too.
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