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http://ir.ncue.edu.tw/ir/handle/987654321/14852
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| 題名: | Reducing Over-dispersion by Generalized Degree of Freedom and Propensity Score |
| 作者: | Lian, Ie-Bin |
| 貢獻者: | 數學系 |
| 關鍵詞: | Bias correction;Logistic regression;Confounder selection |
| 日期: | 2003-06
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| 上傳時間: | 2012-12-10T02:29:20Z
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| 出版者: | Elsevier |
| 摘要: | Assume y is a response variable, x is a risk factor of interest, and z's are covariates, or sometime called "confounders of x" if they are correlated with both x and y. If the covariates are numerous, then model selection procedures are applied on z's while x is usually forced into the model before or after the selection. In this situation, over-dispersion will occur to bias the inference on the relation between x and y. In a linear model, the over-dispersion comes from two sources: an underestimation of the mean-squared error, and a dependency between the estimator of the x-effect and its standard error. The author proposed a method that incorporates the ideas of Ye's generalized degree of freedom and Rosenbaum and Rubin's propensity score. The method reduces the bias and over-dispersion effect to acceptable levels. Data from the Georgia capital charging and sentencing study, which included 1077 observations and 295 covariates, were analyzed as an illustration. |
| 關聯: | Computational Statistics & Data Analysis, 43(2): 197-214 |
| 顯示於類別: | [數學系] 期刊論文
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