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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…
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Related topics
Related topics
9 relations
Basis (linear algebra)
Convex set
Curse of dimensionality
Dimensionality reduction
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Semi-Parametric Causal Sufficient Dimension Reduction Of High Dimensional Treatments
Razieh Nabi
,
I. Shpitser
2017
Corpus ID: 88516066
Cause-effect relationships are typically evaluated by comparing the outcome responses to binary treatment values, representing…
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2015
2015
Pruning a sufficient dimension reduction with a p-value guided hard-thresholding
K. Adragni
,
Mingyu Xi
2015
Corpus ID: 28345817
Principal fitted component (PFC) models are a class of likelihood-based inverse regression methods that yield a so-called…
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2014
2014
General directional regression
Zhou Yu
,
Yuexiao Dong
,
Mian Huang
Journal of Multivariate Analysis
2014
Corpus ID: 36432155
Highly Cited
2011
Highly Cited
2011
Sufficient dimension reduction based on an ensemble of minimum average variance estimators
Xiangrong Yin
,
Bing Li
2011
Corpus ID: 88518698
We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of…
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Highly Cited
2010
Highly Cited
2010
Coordinate-independent sparse sufficient dimension reduction and variable selection
Xin Chen
,
Changliang Zou
,
R. Cook
2010
Corpus ID: 52064202
Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal…
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2010
2010
Sufficient dimension reduction for visual sequence classification
Alex Shyr
,
R. Urtasun
,
Michael I. Jordan
IEEE Computer Society Conference on Computer…
2010
Corpus ID: 11267398
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training…
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2010
2010
A sparse eigen-decomposition estimation in semiparametric regression
Liping Zhu
,
Zhou Yu
,
Li-Xing Zhu
Computational Statistics & Data Analysis
2010
Corpus ID: 32219688
Highly Cited
2009
Highly Cited
2009
Dimension reduction for nonelliptically distributed predictors
Bing Li
,
Yuexiao Dong
2009
Corpus ID: 16179946
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when…
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2008
2008
Sufficient Dimension Reduction With Missing Predictors
Lexin Li
,
Wenbin Lu
2008
Corpus ID: 18856613
In high-dimensional data analysis, sufficient dimension reduction (SDR) methods are effective in reducing the predictor dimension…
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Highly Cited
2007
Highly Cited
2007
Regression on manifolds using kernel dimension reduction
Jens Nilsson
,
Fei Sha
,
Michael I. Jordan
International Conference on Machine Learning
2007
Corpus ID: 14784575
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this…
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