한국통계학회:학술대회논문집 (Proceedings of the Korean Statistical Society Conference)
- 한국통계학회 2003년도 춘계 학술발표회 논문집
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- Pages.167-170
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- 2003
Semiparametric Regression Splines in Matched Case-Control Studies
- Kim, In-Young (Cancer Metastasis Research Center, Yonsei University) ;
- Carroll, Raymond J. (Department of Statistics, Texas A&M University) ;
- Cohen, Noah (Department of Large Animal Medicine and Surgery, Texas A&M University)
- 발행 : 2003.05.23
초록
We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.
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