Partial Least Squares

By T. Cleophas, Aeilko H. Zwinderman. 2013

2,068 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/422074ff0a0401b7749114936ccd7a9b75f0774e

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In Analytical Chemistry

In Statistics and Probability

Citations in the corpus

Trustworthiness in mHealth information services: An assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS) (2011, methodology): “Using the approach of repeated indicators (Wold 1985) in estimating the higher-order latent variable, our study has confirmed adequate measurement & structural results for the research model.”
A model of entrepreneurial intention: An application of the psychological and behavioral approaches (2012, neutral): “This value is both maximally correlated with its own set of indicators and with other latent variables according to the structural/theoretical model (Wold, 1985).”
An assessment of the use of partial least squares structural equation modeling in marketing research (2011, background): “The latter two reasons comply with PLS-SEM’s original purpose of prediction in research contexts with rich data and weak theory (Wold 1985).”
Corporate environmental responsibility, employer reputation and employee commitment: an empirical study in developed and emerging economies (2014, methodology): “In Figure 2, we provide the results of our PLS analysis for the total sample.”
Development and Validation of a Cross-Nationally Stable Scale of Consumer Animosity (2011, positive): “In both studies, analyses are conducted by partial least squares (PLS; Chin 1998; Fornell and Cha 1994; Wold 1982) using the software SmartPLS (Ringle, Wende, and Will 2005).”
Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models (2013, methodology): “(k) Partial least squares (PLS) (see Wold (1985)): this method extracts orthogonal latent factors for the covariates and the outcome and thus indirectly models the inﬂuence of the predictors on the outcome.”
Regularized Generalized Canonical Correlation Analysis (2011, methodology): “…j Jν = , according to formula (7) - Compute new normalized outer weights using formula (9) - Iterate this procedure To obtain a monotonically convergent algorithm, it is necessary to use a sequence of operations similar to the ones used by Wold (1985) and Hanafi (2007) for PLS path modeling.”