Journal article

Publication year: 2007

Pages: 88-108

Journal: Computational Statistics & Data Analysis

Volume number: 52

Issue number: 1

ISSN: 0167-9473

DOI Link: https://doi.org/10.1016/j.csda.2007.01.013

Publisher: Elsevier

Abstract: 

Censored quantile regressions (CQR) are a valuable tool in economics and engineering. The computation of estimators is highly complex and the performance of standard methods is not satisfactory, in particular if a high degree of censoring is present. Due to an interpolation property the computation of CQR estimates corresponds to the solution of a large scale discrete optimization problem. This feature motivates the use of the global optimization heuristic threshold accepting (TA) in comparison to other algorithms. Simulation results presented in this paper indicate that it can improve finding the exact CQR estimator considerably though it uses more computing time.

Harvard Citation style: Fitzenberger, B. and Winker, P. (2007) Improving the computation of censored quantile regressions, Computational Statistics & Data Analysis, 52(1), pp. 88-108. https://doi.org/10.1016/j.csda.2007.01.013

APA Citation style: Fitzenberger, B., & Winker, P. (2007). Improving the computation of censored quantile regressions. Computational Statistics & Data Analysis. 52(1), 88-108. https://doi.org/10.1016/j.csda.2007.01.013