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PLS generalised linear regression

Philippe Bastien, Vincenzo Esposito Vinzi, Michel Tenenhaus
2005 Computational Statistics & Data Analysis  
The approach proposed for PLS generalised linear regression is simple and easy to implement.  ...  The modiÿcations to classical PLS regression, the case of PLS logistic regression and the application of PLS generalised linear regression to survival data are studied in detail.  ...  Table 16 16 Generalised linear regression of y on age, t 1 and each predictor Generalised linear regression of y on age and t 1 PLS model in terms of the original variables.  ... 
doi:10.1016/j.csda.2004.02.005 fatcat:ysza3hzc2zgq5afa2w4urz4p54

Page 5742 of Mathematical Reviews Vol. , Issue 2001H [page]

2001 Mathematical Reviews  
Summary: “Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear.  ...  However, the usual generalised M -estimator of regression has a breakdown point that is less than the inverse of its dimension.  ... 

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

Kevin M. Mendez, Stacey N. Reinke, David I. Broadhurst
2019 Metabolomics  
Historically, partial least squares (PLS) regression has been the gold standard for binary classification.  ...  Importantly, PLS-DA is considered a linear regression method as the final predictive model can be reduced to the standard linear form y * = 0 + 1 x 1 + 1 x 2 + … + n x N , where 0 … N is a vector of PLS  ...  As with PLS, PCR is considered a linear regression method as the independently calculated PCA + MLR coefficients can be combined and reduced to the standard linear form y * = 0 + 1 x 1 + 1 x 2 + … + n  ... 
doi:10.1007/s11306-019-1612-4 pmid:31728648 pmcid:PMC6856029 fatcat:7uze5moxgraspgpueu6sfpuuqa

Rapid and quantitative analysis of metabolites in fermentor broths using pyrolysis mass spectrometry with supervised learning: application to the screening of Penicillium chrysogenum fermentations for the overproduction of penicillins

Royston Goodacre, Sally Trew, Carys Wrigley-Jones, Gunter Saunders, Mark J. Neal, Neil Porter, Douglas B. Kell
1995 Analytica Chimica Acta  
In addition the multivariate linear regression techniques of partial least squares regression (PLS), principal components regression (PCR) and multiple linear regression (MLR) were applied.  ...  inability of models using linear regression accurately to map non-linearities.  ...  To ensure good generalisation the Table 3 Comparison of artificial neural network calibration with partial least squares, principal components regression and multiple linear regression in the deconvolution  ... 
doi:10.1016/0003-2670(95)00170-5 fatcat:nhd2hekuqzfhpnn4fakrpqltom

K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

Max Bylesjö, Mattias Rantalainen, Jeremy K Nicholson, Elaine Holmes, Johan Trygg
2008 BMC Bioinformatics  
Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data.  ...  The PLS package includes an implementation of both linear PLS as well as a linear kernel-based PLS version.  ...  Among the R packages available on CRAN [23] , a few relevant examples include kernlab (kernel-based regression and classification), e1071 (including SVMs) and PLS (implementing a linear kernel-based implementation  ... 
doi:10.1186/1471-2105-9-106 pmid:18284666 pmcid:PMC2323673 fatcat:gkf4swfnbndppn4iuqkrklzrfa

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares

Édith Le Floch, Vincent Guillemot, Vincent Frouin, Philippe Pinel, Christophe Lalanne, Laura Trinchera, Arthur Tenenhaus, Antonio Moreno, Monica Zilbovicius, Thomas Bourgeron, Stanislas Dehaene, Bertrand Thirion (+2 others)
2012 NeuroImage  
Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics.  ...  We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure.  ...  and thus to PLS regression as well on the first component.  ... 
doi:10.1016/j.neuroimage.2012.06.061 pmid:22781162 fatcat:ddcca3dzmfa67ajmm2glhj44ga

Page 464 of Mathematical Reviews Vol. , Issue 2001A [page]

2001 Mathematical Reviews  
We finish by explaining more results on admissibility of linear estimators of vector parameters.” 2001a:62089 62305 62C15 Wojtasik, Leszek (PL-PUZG-IM; Zielona Gora); Zontek, Stefan (PL-ZLG-IM; Zielona  ...  its density), conditional on the value of covariates, x, is given by a mixture of conditional densities of several underly- ing generalised linear models (GLMs).  ... 

Predicting qualitative phenotypes from microarray data – the Eadgene pig data set

Christèle Robert-Granié, Kim-Anh Lê Cao, Magali SanCristobal
2009 BMC Proceedings  
We then compared the classical Partial Least Squares regression (PLS) with a novel approach called sparse PLS, a variant of PLS that adapts lasso penalization and allows for the selection of a subset of  ...  The sparse PLS outperformed the PLS in terms of prediction performance and improved the interpretability of the results.  ...  However, the coefficients that define these components are not linear, as they are solved via successive local regressions on the latent variables.  ... 
doi:10.1186/1753-6561-3-s4-s13 pmid:19615113 pmcid:PMC2712743 fatcat:z5bcaud7yrhlrlrswjlxytzk2y

The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content [article]

B. ODea, T.W. Boonstra, M.E. Larsen, T. Nguyen, S. Venkatesh, H. Christensen
2018 arXiv   pre-print
We then used the multivariate regression model to predict longitudinal changes in mood within subjects.  ...  The lack of robust within-subject correlations indicate that the relationship observed at the group level may not generalise to individual changes over time.  ...  finds a linear regression model.  ... 
arXiv:1811.02750v1 fatcat:3bzfsa4hongm7dwcxbfpgumfbi

Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks

Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke
2020 Metabolomics  
The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful.  ...  We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs.  ...  First, as discussed, a PLS-DA model can be reduced to a single multiple linear regression, Eq. (3), thus feature contribution can be inferred directly from the model's regression coefficients, B PLS .  ... 
doi:10.1007/s11306-020-1640-0 pmid:31965332 pmcid:PMC6974504 fatcat:izyummdmlreffbq5ymtz2vekmu

Quantitative NIR spectroscopy for determination of degree of polymerisation of historical paper

Yun Liu, Tom Fearn, Matija Strlič
2021 Chemometrics and Intelligent Laboratory Systems  
Specifically, partial least squares (PLS) regression was used to predict one of the most important properties of paper as a condition indicatordegree of polymerisation (DP).  ...  The variance of the reference DP, the variance of the predicted DP, and the model bias were identified as the main sources of the total expected generalisation error of prediction.  ...  Among several linear multivariate methods, partial least squares (PLS) regression has been the most important one for quantitative NIR analyses [4] .  ... 
doi:10.1016/j.chemolab.2021.104337 fatcat:ovjy5mzhivdp5a7kq2kcs7hoce

The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study

Bridianne O'Dea, Tjeerd W. Boonstra, Mark E. Larsen, Thin Nguyen, Svetha Venkatesh, Helen Christensen, Ryan L Boyd
2021 PLoS ONE  
Multivariate regression models were used to predict longitudinal changes in symptoms within subjects. A total of 153 participants consented to the study.  ...  The findings also suggest that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time.  ...  finds a linear regression model.  ... 
doi:10.1371/journal.pone.0251787 pmid:34010314 fatcat:ox3vfnq6wbdtxlicntyh5kyeni

Measuring Performance of Selected Algorithms used for Classification and Regression when Applied Against a Standard Dataset

Mr. Vinay S Bharadwaj, Mrs. Sunitha R S, Mr. Shashidhara H S
2017 International Journal of Engineering Research and  
This paper aims at measuring the performance of the few selected algorithms namely, Bayesian Generalized Linear Model, Generalized Linear Model, k-Nearest Neighbours and Partial Least Squares.  ...  Depending on the tabulated results and comparison statistics we conclude that for Pima Indians Diabetes dataset with diabetes as the parameter, the best suited algorithm among the 4 is Generalised Linear  ...  Partial Least Squares The approach here is that we try to project both the predicted output variables and the observable variables into a new space to find the linear regression between the two.  ... 
doi:10.17577/ijertv6is050219 fatcat:6mcyq7p7n5bgdnsfjlumvqjuuq

Page 6191 of Mathematical Reviews Vol. , Issue 96j [page]

1996 Mathematical Reviews  
(BR-SPL-S; Sao Paulo) Second order asymptotics for score tests in generalised linear models. (English summary) Biometrika 82 (1995), no. 2, 426-432.  ...  The authors consider as an important special case the classical linear regression model and show how the corrections work in that case where the exact distribution of Sr is known.  ... 

Page 3155 of Mathematical Reviews Vol. , Issue 98E [page]

1998 Mathematical Reviews  
Kala (PL-POZAG; Poznan) 98e:62098 62J05 62H12 Dougherty, George G., Jr. Estimation of common linear functional relationships in k data sets. (English summary) Biometrika 84 (1997), no. 1, 103-110.  ...  The improvement over ridge regression is moder- ate, but significant, especially when the data are few and noisy.” 98e:62105 62507 62C15 Markiewicz, Augustyn (PL-POZAG-ST; Poznan) Characterization of general  ... 
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