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B0728
Title: Ensemble estimation and variable selection with semiparametric regression models Authors:  Sunyoung Shin - University of Texas at Dallas (United States) [presenting]
Yufeng Liu - University of North Carolina (United States)
Stephen Cole - University of North Carolina at Chapel Hill (United States)
Jason Fine - University of North Carolina at Chapel Hill (United States)
Abstract: Scenarios are considered in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function is difficult to maximize but the components are easy to maximize. As a motivating example, we consider proportional hazards regression with prospective doubly-censored data, in which the likelihood factors into a current status data likelihood and a left-truncated right-censored data likelihood. Variable selection is important in such regression modelling but the applicability of existing techniques is unclear in the ensemble approach. We propose ensemble variable selection using the least squares approximation technique on the unpenalized ensemble estimator, followed by ensemble re-estimation under the selected model. The resulting estimator has the oracle property such that the set of nonzero parameters is successfully recovered and the semiparametric efficiency bound is achieved for this parameter set. Simulations show that the proposed method performs well relative to alternative approaches. Analysis of the multicenter AIDS cohort study illustrates the practical utility of the method.