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Predictive model selection in partial least squares path modeling (PLS-PM)

 

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Opened Access Predictive model selection in partial least squares path modeling (PLS-PM)
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Author: Nidhi Sharma, Pratyush
Shmueli, Galit
Sarstedt, Marko
Kim, Kevin H.
Date: 2015
ISBN/ISSN: 9789036540568
Document type: Presentation
Abstract: Predictive model selection metrics are used to select models with the highest out-of-sample predictive power among a set of models. R2 and related metrics, which are heavily used in partial least squares path modeling, are often mistaken as predictive metrics. We introduce information theoretic model selection criteria that are designed for out-of-sample prediction and which do not require creating a holdout sample. Using a Monte Carlo study, we compare the performance of frequently used model evaluation criteria and information theoretic criteria in selecting the best predictive model under various conditions of sample size, effect size, loading patterns, and data distribution.
Size: 280.8Kb
Format: PDF

URI: http://hdl.handle.net/11441/55513

DOI: 10.3990/2.336

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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