Online Smooth Backfitting for Generalized Additive Models

• 成果类型: Article
• 署名作者: Yang, Ying; Yao, Fang; Zhao, Peng
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Peking University; Jiangsu Normal University; Jiangsu Normal University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2182213
• 发表日期: 2024
• 页码: 1215-1228
• 关键词: approximate bayesian computation; chain monte-carlo; point-processes; prediction; inference; features

摘要

We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general nonlinear optimization problems. The strategy is to use an appropriate-order expansion to approximate the nonlinear equations and store the coefficients as sufficient statistics which can be updated in an online manner by the dynamic candidate bandwidth method. We investigate the statistical and algorithmic convergences of the proposed method. By defining the relative statistical efficiency and computational cost, we further establish a framework to characterize the tradeoff between estimation performance and computation performance. Simulations and real data examples are provided to illustrate the proposed method and algorithm. for this article are available online.