Testing in linear composite quantile regression models

Rong Jiang (), Wei-Min Qian () and Jing-Ru Li ()

Computational Statistics, 2014, vol. 29, issue 5, 1402 pages

Abstract: Composite quantile regression (CQR) can be more efficient and sometimes arbitrarily more efficient than least squares for non-normal random errors, and almost as efficient for normal random errors. Based on CQR, we propose a test method to deal with the testing problem of the parameter in the linear regression models. The critical values of the test statistic can be obtained by the random weighting method without estimating the nuisance parameters. A distinguished feature of the proposed method is that the approximation is valid even the null hypothesis is not true and power evaluation is possible under the local alternatives. Extensive simulations are reported, showing that the proposed method works well in practical settings. The proposed methods are also applied to a data set from a walking behavior survey. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Composite quantile regression; Randomly weighting method; Linear hypothesis (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00180-014-0497-y

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