AIC for Growth Curve Model with Monotone Missing Data

Ayaka Yagi, Takashi Seo, Yasunori Fujikoshi

Research output: Contribution to journalArticlepeer-review

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 (Formula presented.) -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.

Original languageEnglish
Pages (from-to)185-199
Number of pages15
JournalAmerican Journal of Mathematical and Management Sciences
Volume41
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • AIC-type risk
  • maximum likelihood estimator
  • model selection; quasi MLE

Fingerprint

Dive into the research topics of 'AIC for Growth Curve Model with Monotone Missing Data'. Together they form a unique fingerprint.

Cite this