学术报告

报告题目：AdaBoost semiparametric model averaging prediction for multiple categories

报告人：西南大学数学与统计学院 吕晶 博士

时间：2019年3月27日（星期三）下午15:00

地点：25教14楼报告厅

摘要：Model average techniques are very useful for model-based prediction. However most earlier works in this field focused on parametric models and continuous responses. In this talk, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specification of the index variable in the adopted varying coefficient sub-model structure in order to forecast the response. In particular this new SMAP method is more flexible and robust against model mis-specification. To improve the practical predictive performance, we combine SMAP with the AdaBoost algorithm to obtain more accurate estimations of class probabilities and model averaging weights. We compare our proposed methods with all existing model averaging approaches and a wide range of popular classification methods via extensive simulations. An automobile classification study is included to illustrate the merits of our methodology.