Statistical modelling to treat data with missing value

点击翻译按钮获取中文摘要

The purpose of this research was to construct the effective statistical models and to develop the effective method of estimation about the error of the estimates in statistical inference based on incomplete information such as data containing the missing value.We compared the estimates of the variance obtained by the observed information matrix and Bootstrap method for the estimates obtained by EM-algorithm for Poisson mixture model. Kikuchi and Nomakuchi presented this result at the 17th meeting of Japanese Society of Computational Statistics (2003.05).We studied about the estimation of the parameters for normal mixture model and hidden Markov normal model by EM-algorithm and the variance of the estimates by the observed information matrix. Kikuchi and Nomakuchi will present this result at the 20th meeting of Japanese Society of Computational Statistics (2006.06).Kikuchi became the research partaker of the research project of Ministry of Health, Labour and Welfare since 2003. We added a new theme relevant to the application of EM-algorithm to the clinical trial. We developed the method of the estimation of the survival function assuming the Gamma-Weibull distribution and the generalized Gamma distribution. Kikuchi and Nomakuchi presented this result at the 18th meeting of Japanese Society of Computational Statistics (2004.05), and Kikuchi, Nomakuchi and Anraku presented a paper about this result entitled "Estimation of the survival function by EM-algorithm (in Japanese)" on the collected papers of the above research project.