Deep nonlinear sufficient dimension reduction

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Prediction theory (aspects of stochastic processes) (60G25) Neural nets and related approaches to inference from stochastic processes (62M45)

Cites Work

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Measuring and testing dependence by correlation of distances
Efficient estimation in sufficient dimension reduction
Principal support vector machines for linear and nonlinear sufficient dimension reduction
Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
Dimension reduction for nonelliptically distributed predictors
An RKHS formulation of the inverse regression dimension-reduction problem
Regression analysis under link violation
Asymptotics for kernel estimate of sliced inverse regression
Optimal global rates of convergence for nonparametric regression
Weak convergence and empirical processes. With applications to statistics
A general theory for nonlinear sufficient dimension reduction: formulation and estimation
Nonlinear sufficient dimension reduction for functional data
Fusing sufficient dimension reduction with neural networks
Conditional variance estimator for sufficient dimension reduction
Nonparametric regression using deep neural networks with ReLU activation function
A kernel-based measure for conditional mean dependence
Detecting independence of random vectors: generalized distance covariance and Gaussian covariance
Local Rademacher complexities
Contour regression: a general approach to dimension reduction
Estimating the Structural Dimension of Regressions Via Parametric Inverse Regression
Reducing the Dimensionality of Data with Neural Networks
On Directional Regression for Dimension Reduction
Principal Hessian Directions Revisited
Determining the Dimension in Sliced Inverse Regression and Related Methods
Sliced Inverse Regression for Dimension Reduction
Determining the Dimensionality in Sliced Inverse Regression
High-Dimensional Statistics
An Adaptive Estimation of Dimension Reduction Space
Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series
Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening
A new class of measures for testing independence
Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests
Expected Conditional Characteristic Function-based Measures for Testing Independence
Deep Network Approximation Characterized by Number of Neurons
Algorithmic Learning Theory
On Estimation Efficiency of the Central Mean Subspace
A Class of Statistics with Asymptotically Normal Distribution
Fréchet sufficient dimension reduction for random objects
Comment
Generalized martingale difference divergence: detecting conditional mean independence with applications in variable screening
Deep dimension reduction for supervised representation learning

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