title: Automatic structure recovery for additive models.
abstract: We propose an automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy. Our method produces estimates of the set of noise predictors, the sets of predictors that contribute polynomially at different degrees up to a specified degreeM, and the set of predictors that contribute beyond polynomially of degreeM. We prove consistency of the proposed method, and describe an extension to partially linear models. Finite-sample performance of the method is illustrated via Monte Carlo studies and a real-data example.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina 27519, U.S.A.
authors.forename: Yichao
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina 27519, U.S.A.
authors.forename: Leonard A
authors.initials: LA
authors.lastname: Stefanski
keywords: Backfitting; Bandwidth estimation; Kernel; Local polynomial; Measurement-error model selection likelihood; Model selection; Profiling; Smoothing; Variable selection