Results 231 to 240 of about 846,250 (270)

Research on the Stability Model in Discrete Dynamical Systems with the Lorenz Attractor and the Kropotov-Pakhomov Neural Network. [PDF]

Entropy (Basel)
Gospodinova EA.
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Development and validation of a patient-reported-experience measure to ascertain treatment satisfaction in migraine: the MISAT-Q questionnaire. [PDF]

J Patient Rep Outcomes
Gago-Veiga AB, González-García N, Díaz-de-Terán J, Heredia-Rodríguez P, Armada-Peláez B, Moya-Alarcón C, Soto-Álvarez J, Rejas-Gutiérrez J, Ruiz-Díaz MÁ.   +8 more
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Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data

Woollard M, Panwar P, Harland LT, Mo J, Zhang H, Swarbrick A, Gottgens B, Ghazanfar S, Nghiem LH.   +8 more
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Sufficient Dimension Reduction and Graphics in Regression

Annals of the Institute of Statistical Mathematics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CHIAROMONTE, FRANCESCA, Cook RD
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Sparse sufficient dimension reduction

Biometrika, 2007
Existing sufficient dimension reduction methods suffer from the fact that each dimension reduction component is a linear combination of all the original predictors, so that it is difficult to interpret the resulting estimates. We propose a unified estimation strategy, which combines a regression-type formulation of sufficient dimension reduction ...
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Advance of the sufficient dimension reduction

WIREs Computational Statistics, 2020
AbstractThe sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and forward regression methods.
Weiqiang Hang, Yingcun Xia
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Deep nonlinear sufficient dimension reduction

The Annals of Statistics
Summary: Linear sufficient dimension reduction, as exemplified by sliced inverse regression, has seen substantial development in the past thirty years. However, with the advent of more complex scenarios, nonlinear dimension reduction has gained considerable interest recently.
Chen, Yinfeng, Jiao, Yuling, Qiu, Rui, Yu, Zhou   +3 more
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Diagnostic studies in sufficient dimension reduction

Biometrika, 2015
Sufficient dimension reduction in regression aims to reduce the predictor dimension by replacing the original predictors with some set of linear combinations of them without loss of information. Numerous dimension reduction methods have been developed based on this paradigm.
Xin Chen, R. Dennis Cook, Changliang Zou
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Sufficient Dimension Reduction and Kernel Dimension Reduction

2023
Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi   +3 more
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Bayesian Model Averaging Sufficient Dimension Reduction

2020
In 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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