Titlebook: New Perspectives in Partial Least Squares and Related Methods; Herve Abdi,Wynne W. Chin,Laura Trinchera Conference proceedings 2013 Spring

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Two-Step PLS Path Modeling Mode B: Nonlinear and Interaction Effects Between Formative Constructsg the sample size. Significant nonlinear and interaction effects and an increase in the predictability of models are detected with medium or large sample sizes. The procedure is well-suited to estimate nonlinear and interaction effects in structural equation models with formative constructs and few indicators.

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Testing the Differential Impact of Structural Paths in PLS Analysis: A Bootstrapping Approachnative bootstrapping approach. Results from both empirical and simulated data show different conclusions are made between these two approaches. In particular, we show that under data conditions of high kurtosis, bootstrapping is less likely to commit a Type I error of stating substantial differences among paths when none exist.

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PLS Regression and Hybrid Methods in Genomics Association Studieses are used as input for a tree-growing algorithm and a clustering algorithm respectively. We compare these approaches with other classic predictors used in statistical learning, showing that our PLS-based hybrid methods outperform both classic predictors and straightforward PLS regression.

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Conference proceedings 2013st squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS is an abbreviation for Partial Least Squares and is also sometimes expanded as .projection to latent structures.. This is an approach for modeling relations be

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PLS-Based Multivariate Metamodeling of Dynamic Systemstamodeling using nonlinear .-based subspace data modeling. Different types of metamodels are outlined and illustrated. Finally, we discuss some cognitive topics characterizing different modeling cultures. In particular, we tabulate various metaphors deemed relevant for how the time domain is envisioned.

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You Write, but Others Read: Common Methodological Misunderstandings in PLS and Related Methods are prevalent among users and sometimes even appear in premier scholarly journals. In this chapter, we discuss a variety of methodological misunderstandings that warrant careful consideration before indiscriminately applying these methods.

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Correlated Component Regression: Re-thinking Regression in the Presence of Near Collinearityegression). We also present a step-down variable selection algorithm for eliminating irrelevant predictors. Unlike . and penalized regression approaches, . is scale invariant. . is illustrated in several examples involving real data and its performance is compared with other approaches using simulated data.

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