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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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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Propensity Score
Overdispersion
Covariates
Degree of freedom
Dispersion Effect
Selection Procedures
Risk Factors
Standard error
Mean Squared Error
Model Selection
Linear Model
Estimator
Model

All Science Journal Classification (ASJC) codes

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

Cite this

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Reducing over-dispersion by generalized degree of freedom and propensity score. / Lian, Iebin.

In: Computational Statistics and Data Analysis, Vol. 43, No. 2, 28.06.2003, p. 197-214.

Research output: Contribution to journalArticle

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