Results 191 to 200 of about 2,014 (224)

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

Entropy-based sliced inverse regression [PDF]

Computational Statistics and Data Analysis, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Noboru Murata, Hideitsu Hino
exaly   +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

Bagging Versions of Sliced Inverse Regression

Communications in Statistics - Theory and Methods, 2010
Sliced Inverse Regression (SIR) introduced by Li (1991) is a well-known dimension reduction method in semiparametric regression. In this article, we propose bagging versions of SIR which consist in using bootstrap replications of the data set and in aggregating the corresponding estimators.
Saracco, Jérôme, Liquet, Benoit, Kuentz, Vanessa   +2 more
openaire   +4 more sources

Sliced inverse regression for multivariate response regression

Journal of Statistical Planning and Inference, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Heng-Hui Lue
exaly   +3 more sources

Optimal quantization applied to sliced inverse regression [PDF]

Journal of Statistical Planning and Inference, 2012
In this paper we consider a semiparametric regression model involving a $d$-dimensional quantitative explanatory variable $X$ and including a dimension reduction of $X$ via an index $β'X$. In this model, the main goal is to estimate the euclidean parameter $β$ and to predict the real response variable $Y$ conditionally to $X$.
Romain Azais
exaly   +5 more sources

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

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

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   +2 more sources

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.

exaly   +5 more sources