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Sufficient Dimension Reduction and Kernel Dimension Reduction
2023Benyamin Ghojogh +3 more
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Bayesian Model Averaging Sufficient Dimension Reduction
2020In sufficient dimension reduction (Li, 1991; Cook, 1998b), original predictors are replaced by their low-dimensional linear combinations while preserving all of the conditional information of the response given the predictors. Sliced inverse regression [SIR; Li, 1991] and principal Hessian directions [PHD; Li, 1992] are two popular sufficient dimension
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Partial moment-based sufficient dimension reduction
Statistics & Probability Letters, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sufficient Dimension Reduction for Tensor Data
2018With the rapid development of science and technology, a large volume of array data has been collected in areas such as genomics, finance, image processing, and Internet search. How to extract useful information from massive data becomes the key issue nowadays.
Yiwen Liu, Xin Xing, Wenxuan Zhong
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A note on sufficient dimension reduction
Statistics & Probability Letters, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sufficient Dimension Reduction with Missing Data
2017Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991).
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