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Automatic structure recovery for generalized additive models
Author(s) -
Shen Kai,
Wu Yichao
Publication year - 2023
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11739
Subject(s) - poisson regression , generalized additive model , logistic regression , computer science , smoothing , generalized linear model , kernel (algebra) , kernel smoother , monte carlo method , poisson distribution , degree (music) , algorithm , polynomial , additive model , mathematics , kernel method , artificial intelligence , statistics , machine learning , discrete mathematics , mathematical analysis , population , physics , demography , sociology , radial basis function kernel , support vector machine , acoustics
Abstract In this article, we propose an automatic structure recovery method for generalized additive models (GAMs) by extending Wu and Stefanski's approach. In a similar vein, the proposed method is based on a local scoring algorithm coupled with local polynomial smoothing, along with a kernel‐based variable selection approach. Given a specific degree M , the goal is to identify predictors contributing polynomially at different degrees up to M and predictors that contribute beyond degree M . By focusing on two GAMs, logistic regression and Poisson regression, we illustrate the performance of the proposed method using Monte Carlo simulation studies and two real data examples.