Statistical Engineering Division Seminar

Galit Shmueli
Department of Decision & Information Technologies
University of Maryland, College Park, Maryland
Statistical Engineering Division Seminar
Thursday June 15, 2006, 10:30-11:30 AM
NIST North Room 152

Abstract

Electronic commerce has received an extreme surge of popularity in recent years. While the field of economics has created many theories for understanding economic behavior at the individual and market level, many of these theories were developed before the emergence of the world wide web, and do not carry over to the new online environment. Consider online auctions: 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. Luckily, empirical research of eCommerce is blessed by an ever increasing amount of readily-available, high-quality data, and is therefore thriving. However, analysis methods used in this research community for extracting information from data have not kept up with the vast amount and the complex structure of eCommerce data. The currently used statistical methods have typically been limited to "off the shelf" methods such as regression-type modeling.

In this talk, we present a novel statistical approach and set of tools called Functional Data Analysis (FDA) and discuss its usefulness for empirical research of eCommerce. We show how this approach allows the researcher to study, for the first time, dynamic concepts such as process-evolution and, associated with that, process-dynamics in the eCommerce context. We illustrate these ideas by focusing on online auctions, showing that understanding price-evolution and its dynamics can be helpful in characterizing, differentiating, and even forecasting an auction.

There are multiple statistical challenges in applying FDA in the eCommerce context. Many of these arise from the non-standard data structure that arises in typical eCommerce applications. We describe such challenges and some solutions, but also unanswered questions.

Finally, we present interesting results obtained from analyzing online auction data from eBay.com using a functional approach. We show how methods such as curve clustering and functional regression models shed new light on the dynamics that take place in online auctions.

A series of relevant papers is available at http://www.smith.umd.edu/ceme/statistics/papers.html