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E. Bura, D. Kapla, L. Fertl:
"Fusing Sufficient Dimension Reduction with Neural Networks";
Computational Statistics and Data Analysis, Voulme 168 (2022), 107390; 1 - 20.

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 , 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.

Large sample size,Mean subspace,Nonparametric,Prediction,Regression

http://dx.doi.org/10.1016/j.csda.2021.107390