Non-random missing data poses serious problems in longitudinal studies. The binomial distribution parameter becomes to be unidentifiable without any other auxiliary information or assumption when it suffers from ignor...Non-random missing data poses serious problems in longitudinal studies. The binomial distribution parameter becomes to be unidentifiable without any other auxiliary information or assumption when it suffers from ignorable missing data. Existing methods are mostly based on the log-linear regression model. In this article, a model is proposed for longitudinal data with non-ignorable non-response. It is considered to use the pre-test baseline data to improve the identifiability of the post-test parameter. Furthermore, we derive the identified estimation （IE）, the maximum likelihood estimation （MLE） and its associated variance for the post-test parameter. The simulation study based on the model of this paper shows that the proposed approach gives promising results.展开更多
基金Supported by the National Natural Science Foundation of China(No.10801019)the Fundamental ResearchFunds for the Central Universities(BUPT2012RC0708)
文摘Non-random missing data poses serious problems in longitudinal studies. The binomial distribution parameter becomes to be unidentifiable without any other auxiliary information or assumption when it suffers from ignorable missing data. Existing methods are mostly based on the log-linear regression model. In this article, a model is proposed for longitudinal data with non-ignorable non-response. It is considered to use the pre-test baseline data to improve the identifiability of the post-test parameter. Furthermore, we derive the identified estimation （IE）, the maximum likelihood estimation （MLE） and its associated variance for the post-test parameter. The simulation study based on the model of this paper shows that the proposed approach gives promising results.
