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Variable selection in high-dimensional partly linear additive models

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  • Heng Lian

Abstract

Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expansion for the nonparametric part with adaptive lasso penalty on the linear part. Convergence rates as well as asymptotic normality of the linear part are shown. We also perform some Monte Carlo studies to demonstrate the performance of the estimator.

Suggested Citation

  • Heng Lian, 2012. "Variable selection in high-dimensional partly linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 825-839, December.
    • Handle: RePEc:taf:gnstxx:v:24:y:2012:i:4:p:825-839
    DOI: 10.1080/10485252.2012.701300
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    References listed on IDEAS

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    Cited by:

    1. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," CeMMAP working papers 35/15, Institute for Fiscal Studies.
    2. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," Canadian Journal of Economics, Canadian Economics Association, vol. 48(2), pages 389-407, May.
    3. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," CeMMAP working papers CWP35/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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