Quantile-slicing estimation for dimension reduction in regression
From MaRDI portal
Publication:1644423
DOI10.1016/j.jspi.2018.03.001zbMath1432.62096OpenAlexW2810143383MaRDI QIDQ1644423
Hyungwoo Kim, Seung Jun Shin, Yichao Wu
Publication date: 21 June 2018
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2018.03.001
heteroscedasticitysufficient dimension reductionkernel quantile regressionquantile-slicing estimation
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20)
Related Items
On expectile-assisted inverse regression estimation for sufficient dimension reduction, A quantile‐slicing approach for sufficient dimension reduction with censored responses
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Measuring and testing dependence by correlation of distances
- Efficient estimation in sufficient dimension reduction
- A constructive approach to the estimation of dimension reduction directions
- Sliced Regression for Dimension Reduction
- Principal support vector machines for linear and nonlinear sufficient dimension reduction
- Sufficient dimension reduction based on an ensemble of minimum average variance estimators
- An adaptive composite quantile approach to dimension reduction
- GACV for quantile smoothing splines
- Dimension reduction for nonelliptically distributed predictors
- Estimating the dimension of a model
- Principal quantile regression for sufficient dimension reduction with heteroscedasticity
- Regression analysis under link violation
- Determining the dimension of iterative Hessian transformation
- Some results on Tchebycheffian spline functions and stochastic processes
- Contour regression: a general approach to dimension reduction
- Asymptotic properties of sufficient dimension reduction with a diverging number of predictors
- Sufficient Dimension Reduction via Bayesian Mixture Modeling
- Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression
- Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression
- On Directional Regression for Dimension Reduction
- Principal Hessian Directions Revisited
- Sliced Inverse Regression for Dimension Reduction
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- Quantile smoothing splines
- Graphics for Regressions With a Binary Response
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
- An Adaptive Estimation of Dimension Reduction Space
- Dimension Reduction for the Conditionalkth Moment in Regression
- 10.1162/153244302760200713
- A Semiparametric Approach to Dimension Reduction
- Fused Estimators of the Central Subspace in Sufficient Dimension Reduction
- Dimension Reduction in Regressions Through Cumulative Slicing Estimation
- Quantile Regression in Reproducing Kernel Hilbert Spaces
