AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

16 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)495-509
Number of pages15
Journal / PublicationJournal of the American Statistical Association
Volume117
Issue number537
Online published18 Aug 2020
Publication statusPublished - Mar 2022

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.

Research Area(s)

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