An adapted loss function for composite quantile regression with censored data

Xiaohui Yuan, Xinran Zhang, Wei Guo and Qian Hu ()
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Xiaohui Yuan: Changchun University of Technology
Xinran Zhang: Changchun University of Technology
Wei Guo: Changchun University of Technology
Qian Hu: Changchun University of Technology

Computational Statistics, 2024, vol. 39, issue 3, No 12, 1401 pages

Abstract: Abstract This paper investigates an adapted loss function for the estimation of a linear regression with right censored responses. The adapted loss function could be used in composite quantile regression, which is a good method to handle the responses with high censored rate. Under some regular conditions, we establish the consistency and asymptotic normality of the resulting estimator. For estimation of regression parameters, we propose the MMCD algorithm, which generates satisfactory results for the proposed estimator. In addition, the algorithm can also be extended to the fused adaptive lasso penalized method to identify the interquantile commonality. The finite sample performances of the methods are further illustrated by numerical results and the analysis of two real datasets.

Keywords: Adapted loss function; Composite quantile regression; Fused adaptive lasso; MMCD algorithm; Right censoring (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01352-6

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