In statistics, sufficient dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the conceptâ€¦Â (More)

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2015

2015

Correlation structure contains important information about longitudinal data. Existing sufficient dimension reduction approachesâ€¦Â (More)

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2013

2013

- Tao Wang, Lixing Zhu
- Computational Statistics & Data Analysis
- 2013

Sufficient dimension reduction is a body of theory and methods for reducing the dimensionality of predictors while preservingâ€¦Â (More)

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2010

2010

- Alex Shyr, Raquel Urtasun, Michael I. Jordan
- 2010 IEEE Computer Society Conference on Computerâ€¦
- 2010

When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of trainingâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

- Taiji Suzuki, Masashi Sugiyama
- AISTATS
- 2010

The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features thatâ€¦Â (More)

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2008

2008

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given theâ€¦Â (More)

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2008

2008

Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can beâ€¦Â (More)

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2008

2008

In high-dimensional data analysis, sufficient dimension reduction (SDR) methods are effective in reducing the predictor dimensionâ€¦Â (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 objectiveâ€¦Â (More)

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2005

2005

- Liqiang Ni
- 2005

Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this methodâ€¦Â (More)

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2004

2004

- Richard D. Cook
- 2004

We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for theâ€¦Â (More)

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