報告題目：Estimation and Inference for Multi-Kink Quantile Regression
報 告 人：鐘威教授 廈門大學
報告時間：2020年7月7日 9:00-10:00
報告地點：騰訊會議 ID：850 929 796
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https://meeting.tencent.com/s/FAnC7BsqUkGF
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：
This article is concerned with parameter estimation, change points detection and statistical inference for a Multi-Kink Quantile Regression (MKQR) model. It assumes different regression forms in different regions of the domain of the threshold covariate but is still continuous at multiple kink thresholds. We propose an iterative segmented quantile regression algorithm for estimating both the regression coefficients and the locations of kink points. It is much more computationally efficient than the grid search algorithm and not sensitive to the initial values. We theoretically demonstrate that the selection consistency of the number of kink points and the asymptotic normality of both regression coefficients and kink locations parameters. The MKQR model is robust to outliers and heavy-tailed errors in the response and more flexible for modelling data with heterogeneous conditional distributions especially when upper or lower quantiles of the response are of interest. Monte Carlo simulations and two real data applications illustrate the excellent performances of the proposed method.
報告人簡介：
鐘威，現任廈門大學王亞南經濟研究院和經濟學院統計系教授、博士生導師，經濟學院院長助理，國家自然科學基金優秀青年基金獲得者（2019），福建省自然科學傑出青年基金獲得者（2019）。2012年獲得美國賓夕法尼亞州立大學統計學博士學位，2014年和2017年分别破格晉升副教授和教授。主要從事高維數據統計分析和理論、統計學習和數據挖掘算法、計量經濟學、統計學和數據科學的應用等領域的研究。在The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Business & Economic Statistics, Annals of Applied Statistics, Statistica Sinica，中國科學數學等國内外統計學權威期刊發表20多篇論文。擔任美國統計協會(ASA)期刊《Statistical Analysis and Data Mining》的副主編。
