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Semiparametric Regression Splines in Matched Case‐Control Studies
Author(s) -
Kim Inyoung,
Cohen Noah D.,
Carroll Raymond J.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2003.00133.x
Subject(s) - semiparametric regression , bayesian probability , computer science , bayesian linear regression , multivariate adaptive regression splines , regression , regression analysis , spline (mechanical) , smoothing spline , cross validation , monte carlo method , mathematics , econometrics , nonparametric regression , statistics , mathematical optimization , bayesian inference , engineering , structural engineering , bilinear interpolation , spline interpolation
Summary . We develop semiparametric methods for matched case‐control studies using regression splines. Three methods are developed: 1) an approximate cross‐validation scheme to estimate the smoothing parameter inherent in regression splines, as well as 2) Monte Carlo expectation maximization (MCEM) and 3) 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; the approximate cross‐validation method is computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.