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The elephant in the room: Predictive performance of PLS models

机译:房间里的大象:PLS模型的预测性能

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摘要

Attempts to introduce predictive performance metrics into partial least squares (PLS) path modeling have been slow and fall short of demonstrating impact on either practice or scientific development in PLS. This study contributes to PLS development by offering a comprehensive framework that identifies different dimensions of prediction and their effect on predictive performance evaluation with PLS. This framework contextualizes prior efforts in PLS and prediction and highlights potential opportunities and challenges. A second contribution to PLS development lies in proposed procedures to generate and evaluate different types of predictions from PLS models. These procedures account for the best practices that the new framework identifies. An outline of the many powerful ways in which predictive PLS methodologies can strengthen theory-building research constitutes a third contribution to PLS development. The framework, procedures, and research guidelines hopefully form the basis for a more informed and unified development of the rigorous theoretical and practical applications of PLS. (C) 2016 Elsevier Inc. All rights reserved.
机译:将预测性能指标引入偏最小二乘(PLS)路径建模的尝试很慢,并且未能证明对PLS的实践或科学发展有影响。这项研究通过提供一个全面的框架来为PLS的发展做出贡献,该框架可以识别预测的不同维度及其对PLS预测性能评估的影响。该框架结合了PLS和预测中的先前工作,并突出了潜在的机遇和挑战。 PLS开发的第二个贡献在于提议的过程,该过程可从PLS模型生成和评估不同类型的预测。这些过程说明了新框架确定的最佳实践。预测性PLS方法可以加强理论构建研究的许多有效方式的概述构成了PLS发展的第三项贡献。框架,程序和研究指南有望为更严格和更严格的PLS理论和实际应用的开发提供基础。 (C)2016 Elsevier Inc.保留所有权利。

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