威尼斯欢迎你welcome建院40周年系列学术活动第23期
统计学系列 Seminar 第71期
主题: Robust quasi-likelihood estimation for the negative binomial integer-valued GARCH(1,1) model
主讲人: 朱复康
主持人：王国长
地点：威尼斯欢迎你welcome（中惠楼）102室
会议时间：2020年10月16日上午 10:00-11:00
摘要:
For count time series analysis, the Poisson integer-valued generalized autoregressive conditional heteroscedastic model is very popular but is not usually suitable in the existence of potential extreme observations. Maximum likelihood estimator is commonly used to estimate parameters, but it is highly affected by the outliers. This paper has three main aims. First, we apply the negative binomial model in our study for count time series analysis and consider the maximum likelihood estimation of this model. Second, we extend the Mallows' quasi-likelihood method proposed in the generalized linear models to our situation. Besides, we establish the consistency and asymptotic normality for the resulting robust estimators under some regularity conditions. Third, the performances of these robust estimators in the presence of transient shifts and additive outliers are investigated via simulations. We apply the robust estimator to two stock-market data sets and their prediction performances are assessed by in-sample and out-of-sample predictions.
主讲人简介:
朱复康，现任吉林大学数学学院教授、博士生导师、概率统计与数据科学系主任。2008年博士毕业，2013年被破格聘为教授。主要从事时间序列分析和金融统计的研究，已经在Annals of Applied Statistics、Journal of Time Series Analysis等杂志上发表SCI论文40篇(其中第一作者或通讯作者论文32篇)，被他人正式引用490余次，单篇文章最高引用110余次。作为负责人获得省部级以上科研项目9项，其中国家自然科学基金4项。现任中国数学会概率统计学会、中国现场统计研究会等12个学会的理事或常务理事，美国数学会《数学评论》评论员，已经为JRSSB、JBES等50余个SCI杂志审稿100余次。
