Bura, E. (2023, December 18). Fusing Sufficient Dimension Reduction with Neural Networks [Conference Presentation]. 2023 IMS International Conference on Statistics and Data Science (ICSDS), Lisbon, Portugal.
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.
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Forschungsschwerpunkte:
Modeling and Simulation: 50% Fundamental Mathematics Research: 50%
