报告人: 夏小超 (华中农业大学)

时  间:2018年10月15日   上午10:00--11:00

地  点:理科楼 LD202

摘  要: Forecasting and predictive inference are fundamental data analysis tasks. Most studies employ parametric approaches making strong assumptions about the data generating process. On the other hand, while nonparametric models are applied, it is sometimes found in situations involving low signal to noise ratios or large numbers of covariates that their performance is unsatisfactory. We propose a new varying‐coefficient semiparametric model averaging prediction (VC‐SMAP) approach to analyze large data sets with abundant covariates. Performance of the procedure is investigated with numerical examples. Even though model averaging has been extensively investigated in the literature, very few authors have considered averaging a set of semiparametric models. Our proposed model averaging approach provides more flexibility than parametric methods, while being more stable and easily implemented than fully multivariate nonparametric varying‐coefficient models. We supply numerical evidence to justify the effectiveness of our methodology.

报告人简介: 夏小超，华中农业大学教师，于2015年12月获得太阳成集团统计学博士学位，于2017年8月-2018年7月在新加坡国立大学进行博士后(Research Fellow)研究工作。目前主持1项国家自然科学青年基金、 1项湖北省自然科学青年基金和1项校启动基金项目。在国际知名统计刊物Statistica Sinica、 Biometrics、 Scandinavian Journal of Statistics等杂志上发表和录用SCI论文十余篇。为多个SCI杂志提供匿名审稿服务。

公司联系人:张志民

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