Robust variable selection in modal varying-coefficient models with longitudinal. (English) Zbl 1457.62123
Summary: In this article we present a robust and efficient variable selection procedure by using modal regression for varying-coefficient models with longitudinal data. The new method is proposed based on basis function approximations and a group version of the adaptive LASSO penalty, which can select significant variables and estimate the non-zero smooth coefficient functions simultaneously. Under suitable conditions, we establish the consistency in variable selection and the oracle property in estimation. A simulation study and two real data examples are undertaken to assess the finite sample performance of the proposed variable selection procedure.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62J07 | Ridge regression; shrinkage estimators (Lasso) |
| 62G20 | Asymptotic properties of nonparametric inference |
| 62-08 | Computational methods for problems pertaining to statistics |
Keywords:
adaptive Lasso; longitudinal data; modal regression; robust estimation; varying-coefficient modelsReferences:
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