统计与管理学院2018年学术报告第19期

【主 题】 Varying-coefficient semiparametric model averaging prediction

【报告人】 Jialiang Li  副教授

National University of Singapore

【时 间】 2018年06月07日（星期四）15:00-16:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【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.