Input Variable Selection in Neural Network Models

DOI10.1080/03610926.2013.804567zbMath1462.62239OpenAlexW1991055392MaRDI QIDQ5419321

Cira Perna, Francesco Giordano, Michele La Rocca

Publication date: 6 June 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.804567

zbMATH Keywords

neural networksvariable selectionmultiple testingsubsampling

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Hypothesis testing in multivariate analysis (62H15) Bootstrap, jackknife and other resampling methods (62F40) Neural networks for/in biological studies, artificial life and related topics (92B20)

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Cites Work

• Variable selection in neural network regression models with dependent data: a subsampling approach
• Multivariate adaptive regression splines
• Rodeo: Sparse, greedy nonparametric regression
• A WARP-SPEED METHOD FOR CONDUCTING MONTE CARLO EXPERIMENTS INVOLVING BOOTSTRAP ESTIMATORS
• Surface estimation, variable selection, and the nonparametric oracle property
• Universal approximation bounds for superpositions of a sigmoidal function
• Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives
• Automatic Block-Length Selection for the Dependent Bootstrap
• Bayesian Nonparametrics via Neural Networks
• 10.1162/153244303322753616
• Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing

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