Merging two cultures: deep and statistical learning

Cites Work

Fisher lecture: Dimension reduction in regression
Methodology and theory for partial least squares applied to functional data
Global sensitivity analysis for a numerical model of radionuclide migration from the RRC ``Kurchatov Institute radwaste disposal site
Projection pursuit
Model-oriented design of experiments
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Nonparametric regression using deep neural networks with ReLU activation function
Autoencoder asset pricing models
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
Selected Works of David Brillinger
Bayesian Deep Net GLM and GLMM
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
Computer Model Calibration Using High-Dimensional Output
Sliced Inverse Regression for Dimension Reduction
RECENTERED AND RESCALED INSTRUMENTAL VARIABLE ESTIMATION OF TOBIT AND PROBIT MODELS WITH ERRORS IN VARIABLES
Partial Least Squares Estimator for Single-Index Models
Deep learning for finance: deep portfolios
Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
A Statistical View of Some Chemometrics Regression Tools
Big data and partial least‐squares prediction
Likelihood-Based Sufficient Dimension Reduction
The number of partitions of a set of N points in k dimensions induced by hyperplanes
Local Shrinkage Rules, Lévy Processes and Regularized Regression
The Elements of Statistical Learning
Deep learning for spatio-temporal modeling: dynamic traffic flows and high frequency trading
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