Ensemble estimation and variable selection with semiparametric regression models.

ABSTRACT: We consider scenarios 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 may be difficult to maximize, but the components are easy to maximize. It covers settings where the nuisance parameter may be estimated at different rates in the component likelihoods. As a motivating example we consider proportional hazards regression with prospective doubly censored data, in which the like

SUBMITTER: Shin S 

PROVIDER: S-EPMC7228544 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature