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… (More)
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1999-2018
051019992018

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2015
2015
Correlation structure contains important information about longitudinal data. Existing sufficient dimension reduction approaches… (More)
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2013
2013
Sufficient dimension reduction is a body of theory and methods for reducing the dimensionality of predictors while preserving… (More)
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2010
2010
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training… (More)
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Highly Cited
2010
Highly Cited
2010
The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features that… (More)
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2008
2008
We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the… (More)
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2008
2008
Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can be… (More)
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2008
2008
In high-dimensional data analysis, sufficient dimension reduction (SDR) methods are effective in reducing the predictor dimension… (More)
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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… (More)
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2005
2005
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method… (More)
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2004
2004
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the… (More)
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