Title

Model Averaging Based on Rank

Document Type

Article

Publication Date

2017

Abstract

In this paper, we investigate model selection and model averaging based on rank regression. Under mild conditions, we propose a focused information criterion and a frequentist model averaging estimator for the focused parameters in rank regression model. Compared to the least squares method, the new method is not only highly efficient but also robust. The large sample properties of the proposed procedure are established. The finite sample properties are investigated via extensive Monte Claro simulation study. Finally, we use the Boston Housing Price Dataset to illustrate the use of the proposed rank methods.

Identifier

10.1080/02664763.2017.1401051

Publisher

Taylor & Francis

Publication Information

Journal of Applied Statistics

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