Results 231 to 240 of about 2,771,485 (258)

A Sliced Inverse Regression Approach for a Stratified Population

Communications in Statistics - Theory and Methods, 2011
In this article, we consider a semiparametric single index regression model involving a real dependent variable Y, a p-dimensional quantitative covariable X, and a categorical predictor Z which defines a stratification of the population. This model includes a dimension reduction of X via an index X'β.
Marie Chavent, Benoit Liquet, Jérôme Saracco   +2 more
exaly   +5 more sources

On the regularization of Sliced Inverse Regression

, 2010
Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a ...
Girard, Stéphane
core   +3 more sources

Fréchet kernel sliced inverse regression

Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yushen Dong, Yichao Wu
openaire   +1 more source

Localized Sliced Inverse Regression

Journal of Computational and Graphical Statistics, 2010
We develop a supervised dimension reduction method that integrates the idea of localization from manifold learning with the sliced inverse regression framework. We call our method localized sliced inverse regression (LSIR) since it takes into account the local structure of the explanatory variables.
Qiang Wu 0003, Sayan Mukherjee 0001, Feng Liang   +2 more
openaire   +2 more sources

Cluster-based Sliced Inverse Regression

Journal of the Korean Statistical Society, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Saracco, Jérôme, Kuentz, Vanessa
openaire   +4 more sources

Random sliced inverse regression

Communications in Statistics - Simulation and Computation, 2015
ABSTRACTSliced Inverse Regression (SIR; 1991) is a dimension reduction method for reducing the dimension of the predictors without losing regression information. The implementation of SIR requires inverting the covariance matrix of the predictors—which has hindered its use to analyze high-dimensional data where the number of predictors exceed the ...
openaire   +1 more source

Higher‐order sliced inverse regressions

WIREs Computational Statistics, 2015
With the advancement of modern technology, array‐valued data are often encountered in application. Such data can exhibit both high dimensionality and complex structures. Traditional methods for sufficient dimension reduction (SDR) are generally inefficient for array‐valued data as they cannot adequately capture the underlying structure. In this article,
Ding, Shanshan, Cook, R. Dennis
openaire   +2 more sources

Sliced mean variance–covariance inverse regression

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Simon J. Sheather, Joseph W. McKean, Kimberly Crimin   +2 more
openaire   +3 more sources

A note on sliced inverse regression with missing predictors

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2011
AbstractSufficient dimension reduction (SDR) is effective in high‐dimensional data analysis as it mitigates the curse of dimensionality while retaining full regression information. Missing predictors are common in high‐dimensional data, yet are only discussed occasionally in the SDR context. In this paper, an inverse probability weighted sliced inverse
Yuexiao Dong, Liping Zhu
openaire   +3 more sources

A Quasi-residuals method in sliced inverse regression

Statistics & Probability Letters, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tian, Maozai, Li, Guoying
openaire   +1 more source