Online Smooth Backfitting for Generalized Additive Models

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 coef...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 119; no. 546; pp. 1215 - 1228
Main Authors:	Yang, Ying, Yao, Fang, Zhao, Peng
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 02.04.2024 Taylor & Francis Ltd
Subjects:	Additives Algorithmic convergence Algorithms Americans Candidates Computation Computational efficiency cost effectiveness equations Estimation Internet Nonlinear equations Online learning Optimization Statistical efficiency Statistics Streaming data system optimization
ISSN:	0162-1459, 1537-274X, 1537-274X
Online Access:	Get full text
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