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    题名: 含遺失值之列聯表最大概似估計量及模式的探討
    Maximum Likelihood Estimation in Contingency Tables with Missing Data
    作者: 黃珮菁
    Huang, Pei-Ching
    贡献者: 江振東
    Chiang, Jeng-Tung
    黃珮菁
    Huang, Pei-Ching
    关键词: 遺失值
    完全及部分列聯表分析
    單樣本方法
    多樣本方法
    概似方程式因式分解法
    EM演算法
    Missing data
    Completely and partially cross-classified data
    Single-sample method
    Multiple-sample method
    Factorization of the likelihood method
    EM algorithm
    日期: 2000
    上传时间: 2016-03-31 14:44:42 (UTC+8)
    摘要: 在處理具遺失值之類別資料時，傳統的方法是將資料捨棄，但是這通常不是明智之舉，這些遺失某些分類訊息的資料通常還是可以提供其它重要的訊息，尤其當這類型資料的個數佔大多數時，將其捨棄可能使得估計的變異數增加，甚至影響最後的決策。如何將這些遺失某些訊息的資料納入考慮，作出完整的分析是最近幾十年間頗為重要的課題。本文主要整理了五種分析這類型資料的方法，分別為單樣本方法、多樣本方法、概似方程式因式分解法、EM演算法，以上四種方法可使用在資料遺失呈隨機分佈的條件成立下來進行分析。第五種則為樣本遺失不呈隨機分佈之分析方法。
    Traditionally, the simple way to deal with observations for which some of the variables are missing so that they cannot cross-classified into a contingency table simply excludes them from any analysis. However, it is generally agreed that such a practice would usually affect both the accuracy and the precision of the results. The purpose of the study is to bring together some of the sound alternatives available in the literature, and provide a comprehensive review. Four methods for handling data missing at random are discussed, they are single-sample method, multiple-sample method, factorization of the likelihood method, and EM algorithm. In addition, one way of handling data missing not at random is also reviewed.
    參考文獻: Agresti, A. (1990). Categorical Data Analysis. New York：Wiley.
    Agresti, A. (1996). An Introduction to Categorical Data Analysis. New York：Wiley.
    Anderson, T.W. (1964). Maximum likelihood estimates for the multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52, 200-203.
    Blumenthal, S. (1968). Multinomial sampling with partially categorized data. Journal of the American Statistical Association, 63, 542-551.
    Chen, T., and S. E. Fienberg (1974). Two-dimensional contingency tables with both completely and partially cross-classified data. Biometrics, 30, 629-642.
    Chen, T., and S. E. Fienberg (1976). The analysis of contingency tables with incompletely classified data. Biometrics, 32, 133-144.
    Choi, S.C., and D.M. Stablein (1988). Comparing incomplete paired binomial data under non-random mechanisms. Statistics in Medicine, 7, 929-939.
    Clogg, C. C., and E. S. Shihadeh (1994). Statistical Models for Ordinal Variables. SAGE PUBLICATION.
    Fuchs, C. (1982). Maximum likelihood estimation and model selection in contingency tables with missing data. Journal of the American Statistical Association, 77, 270-278.
    Haber, M., and G. D. Williamson (1994). Models for three-dimensional contingency tables with completely and partially cross-classified data. Biometrics, 49, 194-203.
    Haber, M., C. C.H. Chen, and G. D. Williamson (1991). Analysis of repeated categorical responses from fully and partially cross-classified data. Communications in statistics, 20, 3293-3313.
    Hocking, R.R., and H.H. Oxspring (1971). Maximum likelihood estimation with incomplete multinomial data. Journal of the American Statistical Association, 66, 65-70.
    Hocking, R.R., and H.H. Oxspring (1974). The analysis of partially categorized contingency data. Biometrics, 60, 469-483.
    Laird, N. M. (1988). Missing data in longitudinal studies. Statistics in Medicine, 7, 305-315.
    Lipsitz, S. R., J. G. Ibrahim, and G. M. Fitzmaurice (1999). Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates. Biometrics, 55, 214-223.
    Little, R. J.A. (1982). Models for nonresponse in sample surveys. Journal of the American Statistical Association, 77, 237-250.
    Little, R. J.A., and D. B. Rubin (1987). Statistical Analysis with Missing Data. New York：Wiley.
    Nordheim, E. V. (1984). Inference from nonrandomly missing categorical data: an example from a genetic study on Turner’s syndrome. Journal of the American Statistical Association, 79, 772-780.
    Rubin, D. B. (1976). Inference and missing data. Biometrics, 63, 581-592.
    描述: 碩士
    國立政治大學
    統計學系
    87354014
    資料來源: http://thesis.lib.nccu.edu.tw/record/#A2002001939
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

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