Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
The Annals of Applied Statistics · 2008 · Volume 2 · Issue 3
Abstract
We 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.
Keywords
Bidder trajectory, clustering of trajectories, Functional data analysis, metric in function space, multidimensional scaling
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