Implications of confounding from unmodeled interactions between explanatory variables when using latent variable regression models to make inferences
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DOI: 10.1002/cem.3531
Abstract: With linear dependency between the explanatory variables, partial least squares (PLS) regression is commonly used for regression analysis. If the response variable correlates to a high degree with the explanatory variables, a model with excellent… read more here.
Keywords: explanatory variables; make inferences; implications confounding; confounding unmodeled ... See more keywords