AIC for Growth Curve Model with Monotone Missing Data

Abstract

Abstract In this article, we consider an AIC for a one-sample version of the growth curve model when the dataset has a monotone pattern of missing observations. It is well known that the AIC can be regarded as an approximately unbiased estimator of the AIC-type risk defined by the expected -predictive likelihood. Here, the likelihood is based on the observed data. First, when the covariance matrix is known, we derive an AIC, which is an exact unbiased estimator of the AIC-type risk function. Next, when the covariance matrix is unknown, we derive a conventional AIC using the estimators based on the complete data set only. Finally, a numerical example is presented to illustrate our model selection procedure.