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Comparison of multivariate calibration methods for prediction of feeding value by near infrared reflectance spectroscopy

P.W. Goedhart
1990 Netherlands Journal of Agricultural Science  
A linear calibration model was estimated from experimental data and a model was used to predict unknown in vitro values with measured spectra in future samples.  ...  The spectrum of absorbance of near infrared spectroscopy (NIR) measurements contains indirect, non-specific information about the feeding value of the feed sample and can be used to predict this value.  ...  Acknowledgements It is a pleasure to record many helpful discussions on the subject of this paper with Arend Oosterhoorn, Herman Vedder, Pieter Vereijken and Hilko van der Voet.  ... 
doi:10.18174/njas.v38i3b.16570 fatcat:asbjwj6rd5bcbcpgk4l6dohzsy

A PRESS statistic for two-block partial least squares regression [article]

Brian McWilliams, Giovanni Montana
2013 arXiv   pre-print
In practice, the number of latent factors to be retained, which controls the complexity of the model and its predictive ability, has to be carefully selected.  ...  Partial Least Squares (PLS) regression often turns out to be a useful model in these situations since it performs dimensionality reduction by assuming the existence of a small number of latent factors  ...  In PLS regression, model selection has also been commonly performed using Leave-one-out cross validation (LOOCV) [8] and K-fold CV [9] .  ... 
arXiv:1302.5856v1 fatcat:eplvkfppkjctxc2yaheasyd6jy

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

Sandipan Dutta, Susmita Datta, Somnath Datta
2016 Journal of Statistical Computation and Simulation  
Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation.  ...  With the advent of high throughput genomic and proteomic assays, a clinician may intent to use such high dimensional covariates in making better prediction of state occupation.  ...  Penalized regression techniques are widely used in case the covariate dimension is too large compared to the sample size.  ... 
doi:10.1080/00949655.2016.1263992 pmid:29217870 pmcid:PMC5714309 fatcat:c3po7ubqynfxvk5tkcy7bxhduy

Partial least squares proportional hazard regression for application to DNA microarray survival data

D. V. Nguyen, D. M. Rocke
2002 Bioinformatics  
After dimension reduction, the extracted PLS gene components are then used as covariates in a PH regression to predict the survival probabilities.  ...  In this paper we consider the well-known proportional hazard (PH) regression model for survival analysis. Ordinarily, the PH model is used with a few covariates and many observations (subjects).  ...  of Health (P43 ES04699), and the National Cancer Institute (CA90301).  ... 
doi:10.1093/bioinformatics/18.12.1625 pmid:12490447 fatcat:z6qel4xzqjfpho57hsnmgiuiyu

New developments in Sparse PLS regression [article]

Jérémy Magnanensi, Myriam Maumy-Bertrand, Nicolas Meyer, Frédéric Bertrand
2016 arXiv   pre-print
We compare their variable selection reliability and stability concerning tuning parameters determination, as well as their predictive ability, using simulated data for PLS and real microarray gene expression  ...  To overcome this, we have developed a new dynamic bootstrapbased method for significant predictor selection, suitable for both PLS regression and its incorporation into generalized linear models (GPLS)  ...  Second, in order to compare the predictive ability of models, 10-fold CV MSE, related to each selected sparse model through PLS regression, were computed one hundred times.  ... 
arXiv:1601.03281v1 fatcat:fkxk2ahn45hhpn7brlmtf5szfm

Selecting both latent and explanatory variables in the PLS1 regression model

Aziz Lazraq, Robert Cléroux, Jean-Pierre Gauchi
2003 Chemometrics and Intelligent Laboratory Systems  
They are also compared empirically to two other methods that exist in the literature with respect to the quality of fit of the model and to their predictive ability.  ...  The significant PLS components are first obtained and the two predictor selection methods, called PLS -Forward and PLS -Bootstrap, are applied to the PLS model obtained.  ...  This paper has been supported, in part, by the National Sciences and Engineering Research Council of Canada.  ... 
doi:10.1016/s0169-7439(03)00027-3 fatcat:udqyp6y7dnci7lojlvubra5dci

Visible and Near-Infrared Spectroscopy Analysis of a Polycyclic Aromatic Hydrocarbon in Soils

Reuben N. Okparanma, Abdul M. Mouazen
2013 The Scientific World Journal  
Partial least-squares (PLS) regression analysis with full cross-validation was used to develop models to predict the PAH compound.  ...  The mechanism of prediction was attributed to covariation of the PAH with clay and soil organic carbon.  ...  The Rivers State University of Science and Technology, Port Harcourt, Nigeria, also provided support through its Academic Staff Development Program.  ... 
doi:10.1155/2013/160360 pmid:24453798 pmcid:PMC3886336 fatcat:zhi2k3ulerekpduzgoc7uj2zo4

Rapid Characterization and Prediction of Biomass Properties via Statistical Techniques

Hyun-Woo Cho
2012 Clean Technology  
The prediction results of various prediction models and the effect of using preprocessing methods for NIR data are compared using six types of biomass NIR data.  ...  It also turned out that by adopting the use of proper preprocessing methods the performance of prediction of biomass properties improved.  ...  That is, three of the 54 samples was kept out of model development and predicted by the calibration model. Then, this task is repeated until every sample has been excluded only once.  ... 
doi:10.7464/ksct.2012.18.3.265 fatcat:ixen4lpmzrci7p72y4fmv4g7iy

Small Moving Window Calibration Models for Soft Sensing Processes with Limited History [article]

Casey Kneale, Steven D. Brown
2018 arXiv   pre-print
On the majority of datasets studied, the RF-PLS calibration method offered the lowest one-step-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability  ...  partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online.  ...  Conflict of Interest The authors declare no conflict of interest. References  ... 
arXiv:1710.11595v3 fatcat:tu3n2jksszdl7hzwewpm2o232q

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

Kaiguang Zhao, Denis Valle, Sorin Popescu, Xuesong Zhang, Bani Mallick
2013 Remote Sensing of Environment  
We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR).  ...  Compared to PLS and SMR, BMA substantially reduced overfitting and enhanced model generalization; BMA also yielded error estimation better indicative of true uncertainties in predictions, when evaluated  ...  Our Matlab code of the Bayesian model was a modified implementation of the algorithms for the book "Bayesian methods for nonlinear classification and regression" coauthored by Bani Mallick.  ... 
doi:10.1016/j.rse.2012.12.026 fatcat:nfib3obnuzdevitawjnman4oti

Classification Using Generalized Partial Least Squares

Beiying Ding, Robert Gentleman
2005 Journal of Computational And Graphical Statistics  
number of covariates and few samples.  ...  number of covariates and few samples.  ...  Infinite parameter estimates can occur even when there is only one covariate which is highly predictive, hence the problem is due to the model rather than the ability to classify.  ... 
doi:10.1198/106186005x47697 fatcat:s3qzhh3qb5bsnl4zukfl4gatii

Automated Process Quality Monitoring With Prediction Of Fault Condition Using Measurement Data

Hyun-Woo Cho
2013 Zenodo  
Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures.  ...  This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data.  ...  It turned out that the prediction results of the GA-SPPCA model improved significantly compared to other simple prediction models.  ... 
doi:10.5281/zenodo.1058342 fatcat:nkn23uxvdnfq5hyhl6vfkynnwa

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

Alamgir Kabir, Md. Jahanur Rahman, Abu Ahmed Shamim, Rolf D. W. Klemm, Alain B. Labrique, Mahbubur Rashid, Parul Christian, Keith P. West, Mahfuzar Rahman
2017 PLoS ONE  
PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling  ...  It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates  ...  We also compare the performance of PLS regression with PCR and the individual predictive ability of each these two methods.  ... 
doi:10.1371/journal.pone.0189677 pmid:29261760 pmcid:PMC5738092 fatcat:7lrblep66feqjfnahhzt7ycyom

Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data

Maryam Farhadian, Paulo J. G. Lisboa, Abbas Moghimbeigi, Jalal Poorolajal, Hossein Mahjub
2014 The Scientific World Journal  
In microarray studies, the number of samples is relatively small compared to the number of genes per sample.  ...  This naturally calls for the use of a dimension reduction procedure together with the survival prediction model.  ...  Therefore, the authors thank the Vic-chancellor of Research and Technology of Hamadan University of Medical Sciences, Iran, for approving the project and providing financial support.  ... 
doi:10.1155/2014/618412 pmid:25538955 pmcid:PMC4235600 fatcat:npbwclpksnbf3pokzm5u6r7ih4

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  
, and thus provide better predictive models.  ...  Historically, partial least squares (PLS) regression has been the gold standard for binary classification.  ...  Here the generalised predictive ability of an ANN-SS model is compared across three data sets of increasing size.  ... 
doi:10.1007/s11306-019-1612-4 pmid:31728648 pmcid:PMC6856029 fatcat:7uze5moxgraspgpueu6sfpuuqa
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