学术报告

Varying-Cefficient Smiparametric Mdel Aeraging Pediction

阅读次数:1621

题目:Varying-Cefficient Smiparametric Mdel Aeraging Pediction
报告人:Prof. Li Jialiang (新加坡国立大学)
地点:致远楼101室
时间:2018年5月29日 10:00-11:00
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.

欢迎各位参加!
 

联系我们

    电话:86-21-65981384

    地址:上海市四平路1239号 致远楼

Copyright © 2018  同济大学数学科学学院 版权所有.