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    题名: 羅吉斯迴歸模式的診斷方法與探討
    作者: 許瓈云
    贡献者: 江振東
    許瓈云
    关键词: 羅吉斯迴歸模式
    模式診斷
    日期: 2000
    上传时间: 2016-03-30 19:12:49 (UTC+8)
    摘要: 在運用羅吉斯迴歸模式作資料分析時，若是違反了模式的假設，則所做出來的模式都會導致錯誤的統計推論。因此，模式的診斷常常被應用來發掘問題並判斷假設是否合理。本研究是將以往文獻中相關議題的討論做一個有系統的整理，俾便往後的研究者在作羅吉斯迴歸模式診斷時，能有一個可以依循的準則。此外，每種模式診斷的方法皆附上範例及分析過程以供參考。
    When the assumptions of logistic regression analysis are violated, any calculation of a logistic model may lead to invalid statistical inference. Diagnostics are frequently employed to explore problems and determine whether certain assumptions are reasonable. We survey relevant literatures on diagnostics and try to provide a guideline for detecting and correcting violations of logistic regression assumptions.
    參考文獻: Andrews, D. F. and D. Pregibon. (1978). Finding the outliers that matter. Journal of the Royal Statistical Society, Series B40, 85-94.
    Belsley, D. A., E. Kuh. and R. E. Welsch. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley and Sons, New York.
    Christensen, R. (1997). Log-linear Models and Logistic Regression. Springer-Verlag, New York.
    Collett, D. (1991). Modelling Binary Data. Chapman and Hall, London.
    Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19, 15-18.
    Cook, R. D. (1979). Influential observations in linear regression. Journal of the American statistical Association, 74, 169-174.
    Copas, J. B. (1988). Binary regression models for contaminated data (with discussion). Journal of the Royal Statistical Society, Series B50, 225-265.
    Fowlkes, E. B. (1987). Some diagnostics for binary regression via smoothing. Biometrika, 74, 503-505.
    Hoaglin, D. C. and R. E. Welsch. (1978). The hat matrix in regression and ANOVA. The American Statistician, 32, 17-22.
    Hosmer, D. W. and S. Lemeshow. (1980). A goodness-of-fit test for the multiple logistic regression model. Communications in Statistics. A9(10), 1043-1069.
    Hosmer, D. W. and S. Lemeshow. (1989). Applied Logistic Regression. John Wiley and Sons, New York.
    Hosmer, D. W., S. Taber, and S. Lemeshow. (1991). The importance of assessing the fit of logistic regression models: a case study. American Journal of Public Health, 81, 1630-1635.
    Jennings, D. E. (1986). Outliers and residual distributions in logistic regression. Journal of the American Statistical Association, 81, 987-990.
    Kay, R. and S. Little. (1986). Assessing the fit of the logistic model: a case study of children with the haemolytic uraemic syndrome. Applied Statistics, 35, 16-30.
    Kim, C. and K. Jeong. (1993). On the logistic regression diagnostics. Journal of the korean Statistical Society, 22, 27-37.
    Landwehr, J. M., D. Pergibon, and A. C. Shoemaker. (1984). Graphical methods for assessing logistic regression models. Journal of the American statistical Association, 79, 61-71.
    Pregibon, D. (1981). Logistic regression diagnostics. Annals of Statistics, 9, 705-724.
    Ryan, T. P. (1996). Modern Regression Methods. John Wiley and Sons, New York.
    Wang, P. C. (1987). Residual plots for detecting nonlinearity in generalized linear models. Technometrics, 29, 435-438.
    描述: 碩士
    國立政治大學
    統計學系
    86354012
    資料來源: http://thesis.lib.nccu.edu.tw/record/#A2002001931
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

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