Results 21 to 30 of about 181,998 (262)

Multi-trait genome prediction of new environments with partial least squares

Frontiers in Genetics, 2022
The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a
Osval A. Montesinos-López, Abelardo Montesinos-López, David Alejandro Bernal Sandoval, Brandon Alejandro Mosqueda-Gonzalez, Marco Alberto Valenzo-Jiménez, José Crossa, José Crossa   +6 more
doaj   +1 more source

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, Rhyme Rubayet Rudra, Md. Sadmin Nur, Palash Chandra Das   +3 more
doaj   +1 more source

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, Matthew Clyde, Fiona Meek   +2 more
doaj   +2 more sources

PARTIAL LEAST SQUARES REGRESSION $PLS$ ON INTERVAL DATA

Revista de la Facultad de Ciencias, 2016
Uncertainty in the data can be considered as a numerical interval in which a variable can assume its possible values, this has been known as interval data. In this paper the $PLS$ regression methodology is extended to the case where explanatory, response
Carlos Alberto Gaviria-Peña, Raúl Alberto Pérez-Agamez, María Eugenia Puerta-Yepes   +2 more
doaj   +1 more source

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
doaj   +1 more source

Partial least squares for dependent data [PDF]

Biometrika, 2016
The partial least squares algorithm for dependent data realisations is considered. Consequences of ignoring the dependence for the algorithm performance are studied both theoretically and in simulations. It is shown that ignoring certain non-stationary dependence structures leads to inconsistent estimation.
Singer, M., Krivobokova, T., Munk, A., De Groot, B.   +3 more
openaire   +5 more sources

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
doaj   +1 more source

Identification of Browning in Human Adipocytes by Partial Least Squares Regression (PLSR), Infrared Spectral Biomarkers, and Partial Least Squares Discriminant Analysis (PLS-DA) Using FTIR Spectroscopy

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, Se-Jun Park, Suk-Jun Yoon, Yang-Hwan Ryu, Yong Ko   +4 more
doaj   +1 more source

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, Maryam Sadiq, Muhammad Aslam   +2 more
doaj   +1 more source

Partial Least Squares Regression for Binary Data

Mathematics
Classical 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, Elisa Frutos-Bernal, Jose Luis Vicente-Villardon   +2 more
doaj   +1 more source