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Bayesian Multidimensional Scaling and Choice of Dimension

Title
Bayesian Multidimensional Scaling and Choice of Dimension
Authors
Oh M.-S.Raftery A.E.
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2001
Journal Title
Journal of the American Statistical Association
ISSN
0162-1459JCR Link
Citation
vol. 96, no. 455, pp. 1031 - 1044
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
Multidimensional scaling is widely used to handle data that consist of similarity or dissimilarity measures between pairs of objects. We deal with two major problems in metric multidimensional scaling - configuration of objects and determination of the dimension of object configuration - within a Bayesian framework. A Markov chain Monte Carlo algorithm is proposed for object configuration, along with a simple Bayesian criterion, called MDSIC, for choosing their dimension. Simulation results are presented, as are real data. Our method provides better results than does classical multidimensional scaling and ALSCAL for object configuration, and MDSIC seems to work well for dimension choice in the examples considered.
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자연과학대학 > 통계학전공 > Journal papers
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