2020年12月18日下午14:30-16:30，由771771威尼斯.cm大全和771771威尼斯.cm大全岭南统计科学研究院联合主办的“771771威尼斯.cm大全岭南统计学术论坛”在线学术报告第十讲顺利举行。此次线上讲座，老师与同学们通过腾讯会议及在线直播参与研讨。此次讲座邀请了香港大学统计与精算系助理教授、博士生导师朱柯博士。771771威尼斯.cm大全统计系系主任张兴发副教授、统计系程光辉老师等师生参加了讲座。

Testing error distribution by kernelized Stein discrepancy in multivariate time series models

朱柯

朱柯，香港大学统计与精算系, 助理教授、博士生导师，于2011年获得香港科技大学统计学博士学位。主要研究方向为时间序列计量经济和统计，包括稳健统计、拟合优度检验、变点问题、bootstrap方法及应用计量经济。目前，他已经发表学术论文20余篇，其中包括Journal of the American Statistical Association, Annals of Statistics, Journal of the Royal Statistical Society Series B,Journal of Econometrics, Econometric Theory, Journal of Business and Economic Statistics等国际顶尖统计和计量经济学期刊。

摘要：Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of the existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and they thus can not be used to testing for the often observed heavy-tailed and skewed error distributions in applications. In this paper, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data.

在交流提问环节，在线师生积极参与。比如有师生提问势函数在不同假设下的收敛速度区别的问题；临界值的产生是否依赖于维度p，或是与备择假设有关的问题；朱柯博士对师生提出的问题一一给出了详细解答。