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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/14852

Title: Reducing Over-dispersion by Generalized Degree of Freedom and Propensity Score
Authors: Lian, Ie-Bin
Contributors: 數學系
Keywords: Bias correction;Logistic regression;Confounder selection
Date: 2003-06
Issue Date: 2012-12-10T02:29:20Z
Publisher: Elsevier
Abstract: 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.
Relation: Computational Statistics & Data Analysis, 43(2): 197-214
Appears in Collections:[數學系] 期刊論文

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