A maximum likelihood method for an asymmetric MDS model

S. Saburi and N. Chino

Computational Statistics & Data Analysis, 2008, vol. 52, issue 10, 4673-4684

Abstract: A maximum likelihood estimation method is proposed to fit an asymmetric multidimensional scaling model to a set of asymmetric data. This method is based on successive categories scaling, and enables us to analyze asymmetric proximity data measured, at least, at an ordinal scale level. It enables us to examine not only the appropriate scaling level of the data, but also the appropriate dimensionality of the model, using AIC. Prior to or in fitting the asymmetric MDS model, it is important to verify that the data are sufficiently asymmetric. Some variants of symmetry hypotheses are developed for this purpose. Since the emphasis in our paper is not on hypothesis testing, but on model diagnosis, we compare several candidate models including models with these hypotheses based on a similar model comparison idea using AIC. The method is applied to artificial data and a set of friendship data among nations in East Asia and the USA. Relations to other methods are also discussed.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00166-7
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:10:p:4673-4684

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().