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Double-Penalized Quantile Regression in Partially Linear Models

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DOI: 10.4236/ojs.2015.52019    4,439 Downloads   4,987 Views   Citations
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ABSTRACT

In this paper, we propose the double-penalized quantile regression estimators in partially linear models. An iterative algorithm is proposed for solving the proposed optimization problem. Some numerical examples illustrate that the finite sample performances of proposed method perform better than the least squares based method with regard to the non-causal selection rate (NSR) and the median of model error (MME) when the error distribution is heavy-tail. Finally, we apply the proposed methodology to analyze the ragweed pollen level dataset.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Jiang, Y. (2015) Double-Penalized Quantile Regression in Partially Linear Models. Open Journal of Statistics, 5, 158-164. doi: 10.4236/ojs.2015.52019.

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