The K-INDSCAL Model for Heterogeneous Three-Way Dissimilarity Data

Autor/inn/en	Bocci, Laura; Vichi, Maurizio
Titel	The K-INDSCAL Model for Heterogeneous Three-Way Dissimilarity Data
Quelle	In: Psychometrika, 76 (2011) 4, S.691-714 (24 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-011-9225-5
Schlagwörter	Models; Data Analysis; Multidimensional Scaling + Suchen Sie Ihr Suchwort? Analogiemodell; Auswertung; Mehrdimensionale Analysis
Abstract	A weighted Euclidean distance model for analyzing three-way dissimilarity data (stimuli by stimuli by subjects) for heterogeneous subjects is proposed. First, it is shown that INDSCAL may fail to identify a common space representative of the observed data structure in presence of heterogeneity. A new model that removes the rotational invariance of the classical multidimensional scaling problem and specifies K common homogeneous spaces is proposed. The model, called mixture INDSCAL in K classes, or briefly K-INDSCAL, still includes individual saliencies. However, the large number of parameters in K-INDSCAL may produce instability of the estimates and therefore a parsimonious model will also be discussed. The parameters of the model are estimated in a least-squares fitting context and an efficient coordinate descent algorithm is given. The usefulness of K-INDSCAL is demonstrated by both artificial and real data analyses. (Contains 3 tables and 9 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10