Reducing over-dispersion by generalized degree of freedom and propensity score

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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.

Original languageEnglish
Pages (from-to)197-214
Number of pages18
JournalComputational Statistics and Data Analysis
Volume43
Issue number2
DOIs
Publication statusPublished - 2003 Jun 28

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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