Fusing sufficient dimension reduction with neural networks

Daniel Kapla, Lukas Fertl and Efstathia Bura

Computational Statistics & Data Analysis, 2022, vol. 168, issue C

Abstract: Neural networks are combined with sufficient dimension reduction methodology in order to remove the limitation of small p and n of the latter. NN-SDR applies when the dependence of the response Y on a set of predictors X is fully captured by the regression function g(B′X), for an unknown function g and low rank parameter B matrix. It is shown that the proposed estimator is on par with competing sufficient dimension reduction methods, such as minimum average variance estimation and conditional variance estimation, in small p and n settings in simulations. Its main advantage is its scalability in regressions with large data, for which the other methods are infeasible.

Keywords: Large sample size; Mean subspace; Nonparametric; Prediction; Regression (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:168:y:2022:i:c:s0167947321002243

DOI: 10.1016/j.csda.2021.107390

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