Results 31 to 40 of about 232,548 (306)

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 for classification: a new point of view [PDF]

, 2022
Nowadays data are everywhere and it becomes increasingly important to collect and analyze them in the correct way in order to obtain useful information, since a broad number of fields on a scientific and industrial level need data analysis to solve a ...
De Nardi, Martino
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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

Partial Least Squares tutorial for analyzing neuroimaging data [PDF]

Tutorials in Quantitative Methods for Psychology, 2014
Partial least squares (PLS) has become a respected and meaningful soft modeling analysis technique that can be applied to very large datasets where the number of factors or variables is greater than the number of ...
Patricia Van Roon, Jila Zakizadeh, Sylvain Chartier   +2 more
doaj  

Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models

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 , Frédéric Bertrand , Myriam Maumy-Bertrand , Myriam Maumy-Bertrand    +3 more
doaj   +1 more source

Robust Partially-Compressed Least-Squares

CoRR, 2015
Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off.
Stephen Becker, Ban Kawas, Marek Petrik, Karthikeyan Natesan Ramamurthy   +3 more
openaire   +2 more sources

Hospitalization Through Families’ Eyes: Comparing Inpatient Care Quality for Children With Sickle Cell Disease and Cystic Fibrosis in Canada

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Sickle cell disease (SCD) is a chronic, inherited hemoglobinopathy that requires frequent hospitalization for disease‐related complications. Canadian data on inpatient care is limited. This study compared caregiver‐reported hospital experiences of children with SCD to those with cystic fibrosis (CF), a chronic, autosomal recessive ...
Hailey M. Zwicker, Kyle Kemp, Perri Tutelman, Sharon H. J. Hou, Paul Fairie, Maria Santana, Gregory M. T. Guilcher, Taryn Fay‐McClymont, Glenda Bendiak, Wendy Pelletier, Nicola Wright, Fiona Schulte   +11 more
wiley   +1 more source

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, González, Javier, Peña, Daniel   +2 more
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