Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models [PDF]
Frontiers in Big Data, 2021 Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult.
Frédéric Bertrand +3 more
doaj +2 more sources
HB-PLS: A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression [PDF]
Forestry Research, 2021 Gene expression data features high dimensionality, multicollinearity, and non-Gaussian distribution noise, posing hurdles for identification of true regulatory genes controlling a biological process or pathway. In this study, we integrated the Huber loss
Wenping Deng +4 more
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A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression [PDF]
HeliyonThis study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya.
Liyan Rong +8 more
doaj +2 more sources
Estimating Outliers Using the Iterative Method in Partial Least Squares Regression Analysis for Linear Models. [PDF]
المجلة العراقية للعلوم الاحصائيةOutliers affect the accuracy of the estimated parameters of the partial least squares regression model and give unacceptably large residual values. Traditional robust methods (used in ordinary least squares) cannot be used to treat outliers in estimating
Mahammad Bazid, taha ali
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The pls Package: Principal Component and Partial Least Squares Regression in R [PDF]
Journal of Statistical Software, 2007 The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General ...
Bjørn-Helge Mevik
doaj +1 more source
Partial Least Squares Regression for Binary Data
MathematicsClassical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited.
Laura Vicente-Gonzalez +2 more
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On Fuzzy Regression Adapting Partial Least Squares [PDF]
, 2011 Partial Least Squared (PLS) regression is a model linking a dependent variable y to a set of X (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions of simple and multiple regressions. PLS is an alternative to classical regression model when there are many variables or the variables are ...
A. BASARAN +2 more
openaire +3 more sources
Partial least-squares regression for soil salinity mapping in Bangladesh
Ecological Indicators, 2023 Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources ...
Showmitra Kumar Sarkar +3 more
doaj +1 more source
Compressor map regression modelling based on partial least squares [PDF]
Royal Society Open Science, 2018 In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN).
Xu Li +6 more
doaj +1 more source
Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression. [PDF]
Animals (Basel), 2019 Cellesi M +6 more
europepmc +3 more sources