Authors: Rajab, Rima Sheikh
Dražić, Milan
Mladenović, Nenad 
Mladenović, Pavle
Yu, Keming
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Fitting censored quantile regression by variable neighborhood search
Journal: Journal of Global Optimization
Volume: 63
Issue: 3
First page: 481
Last page: 500
Issue Date: 1-Nov-2015
Rank: M21
ISSN: 0925-5001
DOI: 10.1007/s10898-015-0311-6
Quantile regression is an increasingly important topic in statistical analysis. However, fitting censored quantile regression is hard to solve numerically because the objective function to be minimized is not convex nor concave in regressors. Performance of standard methods is not satisfactory, particularly if a high degree of censoring is present. The usual approach is to simplify (linearize) estimator function, and to show theoretically that such approximation converges to optimal values. In this paper, we suggest a new approach, to solve optimization problem (nonlinear, nonconvex, and nondifferentiable) directly. Our method is based on variable neighborhood search approach, a recent successful technique for solving global optimization problems. The presented results indicate that our method can improve quality of censored quantizing regressors estimator considerably.
Keywords: Censored regression | Global optimization | Metaheuristics | Powell estimator | Quantile regression | Variable neighborhood search
Publisher: Springer Link

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