Results 231 to 240 of about 845,542 (252)

Generative AI for Bayesian Computation. [PDF]

Entropy (Basel)
Polson N, Sokolov V.
europepmc   +1 more source

Circuit Design in Biology and Machine Learning. II. Anomaly Detection. [PDF]

Entropy (Basel)
Frank SA.
europepmc   +1 more source

Bedside Clinical Ultrasound Performed by Family Physicians in Adult Patients With Abdominal Pain in a Hospital Emergency Department: Protocol for a Pilot Quasi-Experimental Study.

JMIR Res Protoc
Carbajo Martín L, Párraga-Martínez I, Beltrán-Romero LM, Bernabeu Wittel M, Northern Huelva Health Management Area Research Group.   +4 more
europepmc   +1 more source

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
europepmc   +1 more source

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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 ...
openaire   +2 more sources

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
openaire   +1 more source