JSM 2004 - Toronto

Abstract #301073

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Activity Number: 406
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #301073
Title: Dynamic Profiling of Online Auctions Using Curve Clustering
Author(s): Wolfgang Jank*+
Companies: University of Maryland
Address: 4322 Van Munching Hall, College Park, MD, 20742,
Keywords: functional data analysis ; smoothing ; clustering ; bid sniping ; electronic commerce ; online auction
Abstract:

Electronic commerce, and in particular online auctions, have received an extreme surge of popularity in recent years. While auction theory has been studied for a long time from a game-theory perspective, the electronic implementation of the auction mechanism poses new and challenging research questions. Although the body of empirical research on online auctions is growing, there is a lack of treatment of these data from a modern statistical point of view. We present a new source of rich auction data and introduce an innovative way of modelling and analyzing online bidding behavior. In particular, we use functional data analysis to investigate and scrutinize online auction dynamics. We describe the structure of such data and suggest suitable methods, including data smoothing and curve clustering, that allow one to profile online auctions and display different bidding behavior. We illustrate the methods on a set of eBay auction data and tie our results to observed phenomena like early bidding, bid sniping, and bid shilling in the existing literature on online auctions.

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