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- Title
An adapted loss function for composite quantile regression with censored data.
- Authors
Yuan, Xiaohui; Zhang, Xinran; Guo, Wei; Hu, Qian
- 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.
- Subjects
QUANTILE regression; CENSORING (Statistics); ASYMPTOTIC normality; NUMERICAL analysis; PARAMETER estimation
- Publication
Computational Statistics, 2024, Vol 39, Issue 3, p1371
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-023-01352-6