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Implementing partial least squares
Statistics and Computing, 1995Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent.
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Partial Least Squares Path Modeling
2017Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions.
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Optimized sample-weighted partial least squares
Talanta, 2007In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored.
Lu, Xu +6 more
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Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and ...
Aniruddha, Kembhavi +2 more
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Shrinkage Structure of Partial Least Squares
Scandinavian Journal of Statistics, 2000Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other
Lingjærde, Ole C., Christophersen, Nils
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Partial Least Squares Path Modeling: Updated Guidelines
2017Partial least squares (PLS) path modeling is a variance-based structural equation modeling technique that is widely applied in business and social sciences. It is the method of choice if a structural equation model contains both factors and composites. This chapter aggregates new insights and offers a fresh look at PLS path modeling.
Henseler, Jörg +2 more
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2015
Partial least squares (PLS) path modeling is a variance-based form of structural equation modeling. It is frequently applied in business and social sciences to analyze complex causal-predictive models involving latent variables. PLS path modeling makes only soft assumptions with respect to the data distribution, and is relatively robust in case of ...
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Partial least squares (PLS) path modeling is a variance-based form of structural equation modeling. It is frequently applied in business and social sciences to analyze complex causal-predictive models involving latent variables. PLS path modeling makes only soft assumptions with respect to the data distribution, and is relatively robust in case of ...
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Partial Least Squares Strukturgleichungsmodellierung
2017Joseph F. Hair +5 more
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