JSM 2004 - Toronto

Abstract #300429

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Activity Number: 192
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and Marketing
Abstract - #300429
Title: Bayesian Estimation of Bid Sequences in Internet Auctions Using a Generalized Record-breaking Model
Author(s): Eric T. Bradlow*+ and Young-Hoon Park
Companies: Wharton School and Johnson Graduate School of Management
Address: 3730 Walnut St., Philadelphia, PA, 19104,
Keywords: data augmentation ; latent bidders ; generalized record-breaking
Abstract:

We generalize current research on Bayesian stochastic models for record-breaking problems by allowing for the situation in which the number of unsuccessful attempts between records is unobserved. That is, by viewing a record break as the maximum order statistic from an unseen number of attempts, we are able to infer the distribution from which they are derived. Our approach to addressing this problem is through data augmentation in which we stochastically draw both the number and magnitude of the unsuccessful attempts, and then model the observed record (maximal order statistic) conditional on the nonevents. The motivation for this research was derived from a currently active research topic in both marketing and economics: internet auctions, in which successive bids can be viewed as record-breaking events, but the latent number of bidders and their valuations are unseen. We apply our method to data obtained from an internet auction site and provide detailed inferences.

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