Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions

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dc.creatorPeng, Jie
dc.creatorMüller, Hans-Georg
dc.date2008-05-05
dc.date2008-11-17
dc.date.accessioned2026-07-07T10:18:16Z
dc.date.available2026-07-07T10:18:16Z
dc.descriptionWe propose a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available. Such data occur commonly in longitudinal studies and online trading data. A distance measure then makes it possible to apply distance-based analysis such as classification, clustering and multidimensional scaling for irregularly sampled longitudinal data. Once a suitable distance measure for sparsely sampled longitudinal trajectories has been found, we apply distance-based clustering methods to eBay online auction data. We identify six distinct clusters of bidding patterns. Each of these bidding patterns is found to be associated with a specific chance to obtain the auctioned item at a reasonable price.
dc.descriptionPublished in at http://dx.doi.org/10.1214/08-AOAS172 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0805.0463
dc.identifierhttp://arxiv.org/abs/0805.0463
dc.identifierAnnals of Applied Statistics 2008, Vol. 2, No. 3, 1056-1077
dc.identifierdoi:10.1214/08-AOAS172
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/174134
dc.subjectApplications
dc.subjectMethodology
dc.titleDistance-based clustering of sparsely observed stochastic processes, with applications to online auctions
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