한국통계학회:학술대회논문집 (Proceedings of the Korean Statistical Society Conference)
- 한국통계학회 2005년도 추계 학술발표회 논문집
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- Pages.181-186
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- 2005
On statistical Computing via EM Algorithm in Logistic Linear Models Involving Non-ignorable Missing data
- Jun, Yu-Na (Department of Statistics, Chonnam National University) ;
- Qian, Guoqi (Department of Statistics, La Trobe University) ;
- Park, Jeong-Soo (Department of Statistics, Chonnam National University)
- 발행 : 2005.11.04
초록
Many data sets obtained from surveys or medical trials often include missing observations. When these data sets are analyzed, it is general to use only complete cases. However, it is possible to have big biases or involve inefficiency. In this paper, we consider a method for estimating parameters in logistic linear models involving non-ignorable missing data mechanism. A binomial response and normal exploratory model for the missing data are used. We fit the model using the EM algorithm. The E-step is derived by Metropolis-hastings algorithm to generate a sample for missing data and Monte-carlo technique, and the M-step is by Newton-Raphson to maximize likelihood function. Asymptotic variances of the MLE's are derived and the standard error and estimates of parameters are compared.
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