Automatic structure recovery for additive models

Biometrika. 2015 Jun 2;102(2):381-395. doi: 10.1093/biomet/asu070.


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 degree M, and the set of predictors that contribute beyond polynomially of degree M. 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.

Keywords: Backfitting; Bandwidth estimation; Kernel; Local polynomial; Measurement-error model selection likelihood; Model selection; Profiling; Smoothing; Variable selection.