Results 1 to 10 of about 880,930 (320)

Capturing functional connectomics using Riemannian partial least squares [PDF]

Scientific Reports, 2023
For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies.
Matthew Ryan, Gary Glonek, Jono Tuke, Melissa Humphries   +3 more
doaj   +2 more sources

Penalized partial least squares for pleiotropy [PDF]

BMC Bioinformatics, 2021
Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects.
Camilo Broc, Therese Truong, Benoit Liquet   +2 more
doaj   +5 more sources

A non-linear partial least squares based on monotonic inner relation [PDF]

Frontiers in Physiology
A novel regression model, monotonic inner relation-based non-linear partial least squares (MIR-PLS), is proposed to address complex issues like limited observations, multicollinearity, and nonlinearity in Chinese Medicine (CM) dose-effect relationship ...
Xuepeng Zheng, Bin Nie, Jianqiang Du, Yi Rao, Huan Li, Jiandong Chen, Yuwen Du, Yuchao Zhang, Haike Jin   +8 more
doaj   +2 more sources

Comparison between the Conventional Partial Least Squares (Pls) and the Robust Partial Least Squares (Rpls-Sem) Through Winsorization Approach [PDF]

Journal of Information Technology Management, 2022
This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used
GholamReza Zandi, Fadya Ramadan Shakhim, Zulkifley Mohamed, Amel Saad Alshargawi   +3 more
doaj   +1 more source

Integrative sparse partial least squares [PDF]

Statistics in Medicine, 2021
Partial least squares, as a dimension reduction technique, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variables may weaken estimation performance, the sparse partial least squares (SPLS) technique has been proposed to identify important variables and generate more interpretable ...
Weijuan Liang, Shuangge Ma, Qingzhao Zhang, Tingyu Zhu   +3 more
openaire   +4 more sources

New Developments in Sparse PLS Regression

Frontiers in Applied Mathematics and Statistics, 2021
Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection.
Jérémy Magnanensi, Jérémy Magnanensi, Myriam Maumy-Bertrand, Myriam Maumy-Bertrand, Nicolas Meyer, Frédéric Bertrand, Frédéric Bertrand   +6 more
doaj   +1 more source

Extreme partial least-squares

Journal of Multivariate Analysis, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bousebata, Meryem, Enjolras, Geoffroy, Girard, Stéphane   +2 more
openaire   +3 more sources

Evaluation of Freezing-Induced Changes in Aroma Profiles of Pomegranate Juice by Quantitative Descriptive Sensory Analysis, Gas Chromatography-Mass Spectrometry/Olfactometry, Odor Activity Values, Orthogonal Partial Least Squares-Discriminant Analysis, and Odorant Addition Experiment. [PDF]

Foods
Chen Y, Wang P, He W, Tian H, Geng J, Zhang R, Zhan P.   +6 more
europepmc   +3 more sources

Bayesian Sparse Partial Least Squares [PDF]

Neural Computation, 2013
Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years.
Vidaurre, D., Gerven, M.A.J. van, Bielza, C., Larrañaga, P., Heskes, T.M.   +4 more
openaire   +5 more sources

Novel Kernel Orthogonal Partial Least Squares for Dominant Sensor Data Extraction

IEEE Access, 2020
Orthogonal Partial Least Squares (OPLS) methods are aimed at finding the dominant factors from predictor variables that can maximize cross-covariance between the factors themselves and response variables while a high correlation between them should also ...
Bo-Wei Chen
doaj   +1 more source