The use of partial least squares path modeling in causal inference for archival financial accounting research [PDF]
, 2014 In financial accounting research, multivariate regression is almost exclusively the dominant statistical method. By contrast, Partial Least Squares path modeling is a under-utilized statistical method.
Ali, Mohammad Bilal +2 more
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Envelopes and Partial Least Squares Regression
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2013 SummaryWe build connections between envelopes, a recently proposed context for efficient estimation in multivariate statistics, and multivariate partial least squares (PLS) regression. In particular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which opens the door to pursuing the same goals as PLS but using ...
Cook, R. D., Helland, I. S., Su, Z.
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Fast Multiway Partial Least Squares Regression
IEEE Transactions on Biomedical Engineering, 2019 Multiway array decomposition has been successful in providing a better understanding of the structure underlying data and in discovering potentially hidden feature dependences serving high-performance decoder applications. However, the computational cost of multiway algorithms can become prohibitive, especially when considering large datasets ...
Camarrone, Flavio, Van Hulle, Marc M.
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A linearization method for partial least squares regression prediction uncertainty [PDF]
, 2014 We study a local linearization approach put forward by Romera to provide an approximate variance for predictions in partial least squares regression.
Fearn, T, Zhang, Y
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Tensor Envelope Partial Least-Squares Regression
Technometrics, 2017 Partial least squares (PLS) is a prominent solution for dimension reduction and high-dimensional regressions. Recent prevalence of multidimensional tensor data has led to several tensor versions of the PLS algorithms. However, none offers a population model and interpretation, and statistical properties of the associated parameters remain intractable ...
Zhang, Xin, Li, Lexin
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Deep partial least squares for instrumental variable regression
Applied Stochastic Models in Business and Industry, 2023 AbstractIn this paper, we propose deep partial least squares for the estimation of high‐dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dimension reduction and feature selection from the set of instruments and covariates.
Maria Nareklishvili +2 more
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Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids [PDF]
, 2014 A combination of systematic density functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications.
Maekawa, Tomoya +3 more
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Distributional aspects in partial least squares regression [PDF]
, 1999 This paper presents some results about the asymptotic behaviour of the estimate of a regression model obtained by Partial Least Squares (PLS) Methods.
Romera, Rosario
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Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm
Journal of Applied Mathematics, 2018 Partial least squares regression (PLS regression) is used as an alternative for ordinary least squares regression in the presence of multicollinearity. This occurrence is common in chemical engineering problems.
Abdelmounaim Kerkri +2 more
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Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration [PDF]
, 2017 This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional ...
Cui, C, Fearn, T
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