[RT16] Comparing methods for discovering unobserved heterogeneity in PLS-PM: A Monte Carlo study

Conférence Internationale avec comité de lecture : CFE-CMStatistics 2016, December 2016, pp.xx, Seville, Spain,

Auteurs: G. Russolillo , L. Trinchera

motcle:

Résumé: PLS Path Modeling assumes homogeneity among the observed units. In particular a unique model, i.e. the global model, is estimated for the whole dataset. Real data are often affected by unobserved heterogeneity: A unique model may hide important differences. Looking for homogeneous groups is primordial for such datasets. Response-based Unit Segmentation (REBUS-PLS) and PLS prediction-oriented segmentation (PLS-POS) are two recent approaches for dealing with unobserved heterogeneity in PLS path models. Those two methods share a common idea: both aim at identifying group-specific models with an higher predicting capability compared to the global model. We present a Monte Carlo simulation study for comparing REBUS-PLS and PLS-POS in terms of both prediction and parameter recovering. Moreover we asses their pertinence for high-dimensional data in terms of computational time and robustness of the results.

BibTeX

@inproceedings {
	RT16,
	title	=	"{Comparing methods for discovering unobserved heterogeneity in PLS-PM: A Monte Carlo study}",
	author	=	" G. Russolillo and L. Trinchera ",
	booktitle	=	"{CFE-CMStatistics 2016}",
	year	=	2016,
	month	=	"December",
	pages	=	"xx",
	address	=	"Seville, Spain",
}