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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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