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Partial least squares regression in the social sciences [PDF]
Tutorials in Quantitative Methods for Psychology, 2015 Partial least square regression (PLSR) is a statistical modeling technique that extracts latent factors to explain both predictor and response variation.
Megan L. Sawatsky +2 more
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A robust partial least squares method with applications [PDF]
, 2007 Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points.
Romera, Rosario +2 more
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Neural Legal Outcome Prediction with Partial Least Squares Compression
Stats, 2020 Predicting the outcome of a case from a set of factual data is a common goal in legal knowledge discovery. In practice, solving this task is most of the time difficult due to the scarcity of labeled datasets. Additionally, processing long documents often
Charles Condevaux
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Partial least square regression applied to the QTLMAS 2010 dataset [PDF]
, 2010 Detection of genomic regions affecting traits is a goal in many genetic studies. Studies applying distinct methods for detection of these regions, called quantitative trait loci (QTL), have been described, ranging from single marker regression [1] to ...
Calus, M.P.L. +3 more
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Consistent Partial Least Squares Path Modeling via Regularization
Frontiers in Psychology, 2018 Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (
Sunho Jung, JaeHong Park
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Least squares fitting the three-parameter inverse Weibull density [PDF]
, 2010 The inverse Weibull model was developed by Erto [10]. In practice, the unknown parameters of the appropriate inverse Weibull density are not known and must be estimated from a random sample.
Marušić, Miljenko +5 more
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Ordinal Consistent Partial Least Squares [PDF]
, 2017 In this chapter, we present a new variance-based estimator called ordinal consistent partial least squares (OrdPLSc). It is a promising combination of consistent partial least squares (PLSc) and ordinal partial least squares (OrdPLS), respectively, which
Florian Schuberth +3 more
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Photonics, 2022 We aimed to identify the browning of white adipocytes using partial least squares regression (PLSR), infrared spectral biomarkers, and partial least squares discriminant analysis (PLS-DA) with FTIR spectroscopy instead of molecular biology.
Dong-Hyun Shon +4 more
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Filter-Based Factor Selection Methods in Partial Least Squares Regression
IEEE Access, 2019 Factor discovery of high-dimensional data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved subset factor selection method
Tahir Mehmood +2 more
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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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