The information detection for the generalized additive model

Many non-linear models such as the additive models or varying models are often used to fit the complex data. However, how to select a simplified model in the prediction problem or data interpretation is necessary and challenged. In this work, the concerned regression model consists of many unknown g...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2020-08, Vol.90 (12), p.2267-2290
Main Authors: Huang, San-Teng, Wu, Wei-Ying
Format: Article
Language:English
Subjects:
adaptive Lasso
Additive model
Algorithms
B-spline
Basis functions
Computer simulation
nonlinear
Regression analysis
Regression models
weighted group Lasso
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Summary:Many non-linear models such as the additive models or varying models are often used to fit the complex data. However, how to select a simplified model in the prediction problem or data interpretation is necessary and challenged. In this work, the concerned regression model consists of many unknown group regressor functions, and some of them can be irrelevant for the response variable. To find an adequate and simplified model, an algorithm is developed to search the important regressor functions and their related structures through the introduction of basis functions with the Lasso-type penalized scheme. The performance of the proposed algorithm is evaluated under simulation studies and real data analyses.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1774883