• 응용통계연구
• /
• 제21권4호
• /
• pp.659-664
• /
• 2008

Missing continuous covariates are pervasive in the use of generalized linear models for medical data. Multiple imputation is the most common and easy-to-do method of dealing with missing covariate data. However, there are always serious warnings in using this method. There should be concern to make imputed values more proper. In this paper, proper imputation from posterior predictive distribution is developed for implementing with arbitrary priors. We use empirical distribution of the posterior for approximating the posterior predictive distribution, to sample from it. This method is preferable in comparison with a presented imputation method of us which uses a full model to impute missing values using available software. The proposed methods are implemented on glucocorticoid data.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2000년도 추계학술발표회 논문집
• /
• pp.51-56
• /
• 2000

When the standard inference is to be used with complete data and nonresponse is ignorable, then multiple imputations should be created as repetitions under a Bayesian normal model. Many Bayesian models besides the normal, however, approximately yield the standard inference with complete data and thus many such models can be used to create proper imputations. We consider the Bayesian bootstrap (BB) application.