Variable selection for semiparametric varying coefficient partially linear errors-in-variables models. (English) Zbl 1190.62090
Summary: This paper focuses on variable selection for semiparametric varying coefficient partially linear models when the covariates in the parametric and nonparametric components are all measured with errors. A bias-corrected variable selection procedure is proposed by combining basis function approximations with shrinkage estimations. With appropriate selection of the tuning parameters, the consistency of the variable selection procedure and the oracle property of the regularized estimators are established. A simulation study and a real data application are undertaken to evaluate the finite sample performance of the proposed method.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62G20 | Asymptotic properties of nonparametric inference |
| 65C60 | Computational problems in statistics (MSC2010) |
| 62G05 | Nonparametric estimation |
Keywords:
semiparametric varying coefficient partially linear model; variable selection; measurement errors; shrinkage estimationReferences:
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