Results 111 to 120 of about 11,532 (157)

Quantification of PET activation in adipose tissue from non-contrast CT scans. [PDF]

NPJ Digit Med
Cano-Espinosa C, Subrize MW, Franquet E, Cypess AM, Kolodny G, Washko GR, San José Estépar R.   +6 more
europepmc   +1 more source

Survey on Pain Detection Using Machine Learning Models: Narrative Review. [PDF]

JMIR AI
Fang R, Hosseini E, Zhang R, Fang C, Rafatirad S, Homayoun H.   +5 more
europepmc   +1 more source

Downregulation of RORα by alcohol promotes TGFβ and α-SMA expression in mouse lung fibroblasts. [PDF]

Front Med (Lausanne)
Fan X, Tao H, Kang BY, Diaz N, Baba K, Tosini G, Guo J, Yeligar SM, Sueblinvong V.   +8 more
europepmc   +1 more source

Slicing Regression: Dimension Reduction via Inverse Regression

, 1991
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Entropy-based sliced inverse regression

Entropy-based sliced inverse regression
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Sliced Inverse Regression with Regularizations

Biometrics, 2008
SummaryIn high‐dimensional data analysis, sliced inverse regression (SIR) has proven to be an effective dimension reduction tool and has enjoyed wide applications. The usual SIR, however, cannot work with problems where the number of predictors,p, exceeds the sample size,n, and can suffer when there is high collinearity among the predictors.
Li, Lexin, Yin, Xiangrong
openaire   +3 more sources

Fréchet kernel sliced inverse regression

Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dong, Yushen, Wu, Yichao
openaire   +1 more source

Higher‐order sliced inverse regressions

WIREs Computational Statistics, 2015
With the advancement of modern technology, array‐valued data are often encountered in application. Such data can exhibit both high dimensionality and complex structures. Traditional methods for sufficient dimension reduction (SDR) are generally inefficient for array‐valued data as they cannot adequately capture the underlying structure. In this article,
Ding, Shanshan, Cook, R. Dennis
openaire   +2 more sources

Sparse Sliced Inverse Regression

Technometrics, 2006
Sliced inverse regression (SIR) is an innovative and effective method for dimension reduction and data visualization of high-dimensional problems. It replaces the original variables with low-dimensional linear combinations of predictors without any loss of regression information and without the need to prespecify a model or an error distribution ...
Lexin Li, Christopher J Nachtsheim
openaire   +1 more source