Abstract #301634

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JSM 2003 Abstract #301634
Activity Number: 291
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301634
Title: Characterization and Modeling of Internet Shopping Data Distributions
Author(s): Jonathan E. Francis*+ and Victor N. Morin
Companies: Amazon.com and Amazon.com
Address: 18 Dravus St. Apt. 202, Seattle, WA, 98109-1680,
Keywords: count data ; Poisson ; negative binomial ; zero inflated regression ; overdispersion ; internet
Abstract:

Internet shopping count data distributions are typically nonnormal with many outliers, contain a heavy proportion of zero values, and are marked by overdispersion caused by heterogeneity. The data are also marked by strong temporal patterns including autocorrelation, daily cycles, day of week, weekly, and seasonal trends complicating the task of characterizing the data distributions. This paper reviews ongoing research to characterize high- volume internet shopping data, the results of various data modeling efforts, and summarizes the results of power test simulations for several analysis approaches. Various techniques for reducing the high degree of overdispersion in internet shopping count data distributions are discussed.

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