A note on variable selection in functional regression via random subspace method

Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors....

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Bibliographic Details
Published in:Statistical methods & applications 2018-08, Vol.27 (3), p.455-477
Main Authors: Smaga, Łukasz, Matsui, Hidetoshi
Format: Article
Language:English
Subjects:
Chemistry and Earth Sciences
Computer Science
Computer simulation
Economics
Error correction
Finance
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Monte Carlo simulation
Original Paper
Physics
Regression analysis
Regression models
Sampling methods
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Subspace methods
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Summary:Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors. The functional model can be represented by certain multiple linear regression model via basis expansions of functional variables. Based on this model and random subspace method of Mielniczuk and Teisseyre (Comput Stat Data Anal 71:725–742, 2014 ), two simple variable selection procedures for scalar response functional regression model are proposed. The final functional model is selected by using generalized information criteria. Monte Carlo simulation studies conducted and a real data example show very satisfactory performance of new variable selection methods under finite samples. Moreover, they suggest that considered procedures outperform solutions found in the literature in terms of correctly selected model, false discovery rate control and prediction error.
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-018-0421-7