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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Vydané v:Journal of the American Statistical Association Ročník 119; číslo 546; s. 1215 - 1228
Hlavní autori: Yang, Ying, Yao, Fang, Zhao, Peng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Alexandria Taylor & Francis 02.04.2024
Taylor & Francis Ltd
Predmet:
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
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Abstract 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. Supplementary materials for this article are available online.
AbstractList 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. Supplementary materials for this article are available online.
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. Supplementary materials for this article are available online.
Author Zhao, Peng
Yang, Ying
Yao, Fang
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Snippet We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general...
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SubjectTerms 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
Title Online Smooth Backfitting for Generalized Additive Models
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