An Inverse-regression Method of Dependent Variable Transformation for Dimension Reduction with Non-linear Confounding
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Publication:2949875
DOI10.1111/sjos.12135zbMath1360.62375OpenAlexW2136445643MaRDI QIDQ2949875
Publication date: 5 October 2015
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/sjos.12135
dimension reductionnonlinear regressiongraphicssliced inverse regressionprincipal Hessian directionsnonlinear confounding
Factor analysis and principal components; correspondence analysis (62H25) Generalized linear models (logistic models) (62J12) General nonlinear regression (62J02)
Cites Work
- On the conditional distributions of low-dimensional projections from high-dimensional data
- On almost linearity of low dimensional projections from high dimensional data
- A characterization of spherical distributions
- Estimation of the mean of a multivariate normal distribution
- Nonlinear confounding in high-dimensional regression
- Dimension reduction for conditional mean in regression
- Influence Functions for Dimension Reduction Methods: An Example Influence Study of Principal Hessian Direction Analysis
- Principal Hessian Directions for regression with measurement error
- Principal Hessian Directions Revisited
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- On the Interpretation of Regression Plots
- Reweighting to Achieve Elliptically Contoured Covariates in Regression
- A Systematic Approach to the Analysis of Complex Interaction Patterns in Two-Level Factorial Designs
- Model-Free Variable Selection
- Sparse sufficient dimension reduction
- Marginal tests with sliced average variance estimation
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