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| 研究生： |
許瓈云 |
|---|---|
| 論文名稱： |
羅吉斯迴歸模式的診斷方法與探討 |
| 指導教授： | 江振東 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 統計學系 Department of Statistics |
| 論文出版年： | 2000 |
| 畢業學年度： | 88 |
| 語文別： | 中文 |
| 論文頁數： | 70 |
| 中文關鍵詞： | 羅吉斯迴歸模式 、模式診斷 |
| 相關次數： | 點閱：1144 下載：152 |
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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.
封面頁
證明書
致謝詞
論文摘要
目錄
表目錄
圖目錄
第一章 緒論
第一節 研究動機與目的
第二節 相關文獻
第三節 本文架構
第二章 羅吉斯迴歸模式的基本理論
第一節 一般線性迴歸模式的基本架構
第二節 羅吉斯迴歸模式的基本架構
第三節 羅吉斯迴歸模式與線性迴歸模式之比較
第三章 羅吉斯迴歸模型的診斷方法
第一節 診斷二項資料的模式
第二節 診斷二元資料的模式
第三節 其他說明
第四章 實證分析
第一節 二項資料的診斷
第二節 二元資料的診斷
第五章 總結
參考文獻
附錄
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.