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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 3/2018

10.11.2017 | Regular Article

Non-symmetrical composite-based path modeling

verfasst von: Pasquale Dolce, Vincenzo Esposito Vinzi, Natale Carlo Lauro

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 3/2018

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Abstract

Partial least squares path modeling presents some inconsistencies in terms of coherence with the predictive directions specified in the inner model (i.e. the path directions), because the directions of the links in the inner model are not taken into account in the iterative algorithm. In fact, the procedure amplifies interdependence among blocks and fails to distinguish between dependent and explanatory blocks. The method proposed in this paper takes into account and respects the specified path directions, with the aim of improving the predictive ability of the model and to maintain the hypothesized theoretical inner model. To highlight its properties, the proposed method is compared to the classical PLS path modeling in terms of explained variability, predictive relevance and interpretation using artificial data through a real data application. A further development of the method allows to treat multi-dimensional blocks in composite-based path modeling.
Vorheriger Artikel Tree-structured modelling of categorical predictors in generalized additive regression
Nächster Artikel Outlier detection in interval data

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Fußnoten
1
In particular, when new Mode A is applied, outer weights are normalized to unit length.
 
2
Weights are then standardized such that the resulting composite has unit variance.
 
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Metadaten
Titel
Non-symmetrical composite-based path modeling
verfasst von
Pasquale Dolce
Vincenzo Esposito Vinzi
Natale Carlo Lauro
Publikationsdatum
10.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 3/2018
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-017-0302-1

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