AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories

• 成果类型: Article
• 署名作者: Li, Jialiang; Lv, Jing; Wan, Alan T. K.; Liao, Jun
• 署名单位: National University of Singapore; Southwest University - China; City University of Hong Kong; Renmin University of China
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1790375
• 发表日期: 2022
• 页码: 495-509
• 关键词: GENERALIZED LINEAR-MODELS; nonconcave penalized likelihood; varying coefficient models; variable selection; statistical view; Dimension Reduction; evidence contrary; logistic-regression; jmlr 9; CLASSIFICATION

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 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.for this article are available online.