AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories

Abstract

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 article, 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 to forecast the response. In particular, this new SMAP method is more flexible and robust against model misspecification. 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. Supplementary materials for this article are available online.

KEYWORDS:

• Boosting
• Model averaging
• Model misspecification
• Prediction accuracy
• Smoothing
• Vary coefficient structure identification

Additional information

Funding

Jing Lv’s work is partially supported by the National Natural Science Foundation of China grant 11801466, the Basic and Frontier Research Program of Chongqing grant cstc2017jcyjAX0182, and the Fundamental Research Funds for the Central Universities grant XDJK2019C105. Jialiang Li’s work is partially supported by Academic Research Funds R-155-000-205-114, R-155-000-195-114 and Ministry of Education Tier 2 funds in Singapore MOE2017-T2-2-082: R-155-000-197-112 (Direct cost) and R-155-000-197- 113 (IRC). Alan Wan’s work is partially supported by a General Research Fund from the Hong Kong Research Grants Council (Grant No: CityU-11500419).