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Graph informed sliced inverse regression [PDF]

Computational Statistics and Data Analysis, 2021
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Andreas Artemiou, Eugen Pircalabelu
exaly   +3 more sources

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Sparse Sliced Inverse Regression

Technometrics, 2006
Sliced inverse regression (SIR) is an innovative and effective method for dimension reduction and data visualization of high-dimensional problems. It replaces the original variables with low-dimensional linear combinations of predictors without any loss of regression information and without the need to prespecify a model or an error distribution ...
Lexin Li, Christopher John Nachtsheim
exaly   +2 more sources

Sliced Inverse Regression with Regularizations

Biometrics, 2008
SummaryIn high‐dimensional data analysis, sliced inverse regression (SIR) has proven to be an effective dimension reduction tool and has enjoyed wide applications. The usual SIR, however, cannot work with problems where the number of predictors,p, exceeds the sample size,n, and can suffer when there is high collinearity among the predictors.
Xiangrong Yin, Lexin Li
exaly   +4 more sources

A note on shrinkage sliced inverse regression

Biometrika, 2005
Summary: We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model.
Ni, Liqiang, Cook, R. Dennis, Tsai, Chih Ling   +2 more
core   +6 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.
Benoit Liquet, Jérôme Saracco
exaly   +5 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

Influence Functions for Sliced Inverse Regression

Scandinavian Journal of Statistics, 2005
The sliced inverse regression is a technique of estimation of the best linear index coefficients \(\beta_i\) in a nonparametric regression model \(y=f(\beta_1x,\dots,\beta_k x,\varepsilon)\), where \(y\) is the response, \(x\) is the vector of regressors, \(f\) is an unknown regression function, and \(\varepsilon\) is an error term.
Luke Prendergast
exaly   +3 more sources

Functional sliced inverse regression analysis

Statistics, 2003
Most of the usual multivariate methods have been extended to the context of functional data analysis. Our contribution concerns the study of sliced inverse regression (SIR) when the response variable is real but the regressor is a function. In the first part, we show how the relevant properties of SIR remain essentially the same in the functional ...
L. Ferré, A. F. Yao
exaly   +2 more sources