Results 11 to 20 of about 232,548 (306)
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 +2 more
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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 +3 more
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Penerapan Partial Least Squares Pada Data Gingerol
ComTech, 2010 Multivariate calibration model aims to predict the expensive measures obtained by using the measures of a cheap and easy. There are several problems that often occur in the model calibration, among others, and multikolinear. To overcome these problems we
Margaretha Ohyver
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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.
Bielza Lozoya, María Concepción +3 more
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A non-linear partial least squares based on monotonic inner relation [PDF]
Frontiers in PhysiologyA 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 +8 more
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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 +3 more
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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 +3 more
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Kernel Partial Least Squares for Stationary Data
J. Mach. Learn. Res., 2017 We consider the kernel partial least squares algorithm for non-parametric regression with stationary dependent data. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a ...
Krivobokova, Tatyana +2 more
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Journal of Multivariate Analysis, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bousebata, Meryem +2 more
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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 +6 more
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