Pages that link to "Item:Q1952450"

The following pages link to A general theory for nonlinear sufficient dimension reduction: formulation and estimation (Q1952450):

Displaying 38 items.

• Predictive power of principal components for single-index model and sufficient dimension reduction (Q391676) (← links)
• Multiple-population shrinkage estimation via sliced inverse regression (Q517384) (← links)
• Inverse regression-based uncertainty quantification algorithms for high-dimensional models: theory and practice (Q726930) (← links)
• A brief review of linear sufficient dimension reduction through optimization (Q826971) (← links)
• On expectile-assisted inverse regression estimation for sufficient dimension reduction (Q830708) (← links)
• A dimension reduction approach for conditional Kaplan-Meier estimators (Q1616695) (← links)
• Principal quantile regression for sufficient dimension reduction with heteroscedasticity (Q1657946) (← links)
• Nonlinear multi-output regression on unknown input manifold (Q1680851) (← links)
• Principal weighted logistic regression for sufficient dimension reduction in binary classification (Q1740307) (← links)
• A general theory for nonlinear sufficient dimension reduction: formulation and estimation (Q1952450) (← links)
• Dimension reduction for functional data based on weak conditional moments (Q2119221) (← links)
• Minimal \(\sigma\)-field for flexible sufficient dimension reduction (Q2137787) (← links)
• Nonlinear predictive directions in clinical trials (Q2157508) (← links)
• Central subspaces review: methods and applications (Q2172454) (← links)
• Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
• Generalized kernel-based inverse regression methods for sufficient dimension reduction (Q2189615) (← links)
• On principal graphical models with application to gene network (Q2242158) (← links)
• On the predictive potential of kernel principal components (Q2283584) (← links)
• Nonlinear and additive principal component analysis for functional data (Q2657189) (← links)
• A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI (Q3121557) (← links)
• (Q4969096) (← links)
• On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis (Q4975569) (← links)
• Sliced Inverse Regression in Metric Spaces (Q5040480) (← links)
• A tractable latent variable model for nonlinear dimensionality reduction (Q5073081) (← links)
• Statistical learning on emerging economies (Q5139005) (← links)
• Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections (Q5434072) (← links)
• A new reproducing kernel‐based nonlinear dimension reduction method for survival data (Q6049799) (← links)
• A Review of Envelope Models (Q6064133) (← links)
• A selective review of sufficient dimension reduction for multivariate response regression (Q6105773) (← links)
• An improved sufficient dimension reduction-based kriging modeling method for high-dimensional evaluation-expensive problems (Q6120130) (← links)
• Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
• On a nonlinear extension of the principal fitted component model (Q6168914) (← links)
• Covariance-based low-dimensional registration for function-on-function regression (Q6541818) (← links)
• On sufficient dimension reduction for functional data: inverse moment-based methods (Q6600368) (← links)
• Deep nonlinear sufficient dimension reduction (Q6608686) (← links)
• Gradient-based approach to sufficient dimension reduction with functional or longitudinal covariates (Q6641031) (← links)
• A Bayesian variation of Basu's theorem and its ramification in statistical inference (Q6648793) (← links)
• Dimension Reduction for Fréchet Regression (Q6651376) (← links)