bet体育Abstract: This paper proposes a Multi-Kink Quantile Regression (MKQR) model which assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points. First, we investigate parameter estimation, kink points detection and statistical inference in MKQR models. We propose an iterative segmented quantile regression algorithm for estimating both the regression coefficients and the locations of kink points. The proposed algorithm is much more computationally efficient than the grid search algorithm and not sensitive to the selection of initial values. Second, asymptotic properties, such as selection consistency of the number of kink points and asymptotic normality of the estimators of both regression coefficients and kink effects, are established to justify the proposed method theoretically. Third, a score test based on partial subgradients is developed to verify whether the kink effects exist or not. Test-inversion confidence intervals for kink location parameters are also constructed. Monte Carlo simulations and two real data applications on the secondary industrial structure of China and the triceps skinfold thickness of Gambian females illustrate the excellent finite sample performances of the proposed MKQR model. Last, a new R package MultiKink is developed to easily implement the proposed methods.
报告人介绍: 钟威，厦门大学王亚南经济研究院和经济学院统计系教授、博士生导师。主要从事高维数据统计分析和理论、统计学习和数据挖掘算法、计量经济学内生问题、统计学和数据科学的应用等领域的研究。2019年获得国家自然科学基金优秀青年基金。担任美国统计协会(ASA)统计学和数据科学期刊《Statistical Analysis and Data Mining》的副主编，在The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Business & Economic Statistics, Annals of Applied Statistics, Statistica Sinica，中国科学数学等国内外统计学权威期刊发表（含接收）20篇论文，其中入选ESI前1%高被引论文2篇。