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Sufficient dimension reduction

In statistics, sufficient dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept… Expand
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Papers overview

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2017
2017
Dimension reduction and variable selection play important roles in high dimensional data analysis. Minimum Average Variance… Expand
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2014
2014
This article proposes a novel approach to linear dimension reduction for regression using nonparametric estimation with positive… Expand
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2011
2011
We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of… Expand
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Highly Cited
2010
Highly Cited
2010
Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal… Expand
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Highly Cited
2009
Highly Cited
2009
We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the… Expand
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Highly Cited
2009
Highly Cited
2009
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when… Expand
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Highly Cited
2005
Highly Cited
2005
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimizing a quadratic objective… Expand
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Highly Cited
2004
Highly Cited
2004
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the… Expand
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2004
2004
Sufficient dimension reduction (SDR) approaches such as ordinary least squares (OLS), sliced inverse regression, sliced average… Expand
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Highly Cited
2002
Highly Cited
2002
Though partial sliced inverse regression (partial SIR: Chiaromonte et al. [2002. Sufficient dimension reduction in regressions… Expand
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