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Activity Number: 246
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #320764
Title: A Longitudinal Study of Broadway Show Success in a Hierarchical Bayes Framework
Author(s): Lan Nygren* and Kjell Nygren and Jeffrey Simonoff
Companies: Rider University and Navigant Consulting and New York University
Keywords: Longitudinal study ; Broadway shows ; hierarchical Bayes framework
Abstract:

Using a long panel of nine years data, we investigate the factors that are useful for predicting the weekly attendance at Broadway shows. This is accomplished by developing a longitudinal model taking into account the capacity constraints and informative dropout in a hierarchical Bayes framework. The weekly show attendance is modeled using a re-parameterized gamma distribution with parameters representing opening week performance, timing of peak performance and decay rate. Our research indicates that opening week performance is strongly related to the show type, whether a show is a revival, and the number of star actors. Shows that received positive reviews from New York Times take longer time to reach peak performance. Weekly demand declines faster for a play and more slowly for a show that received a positive review from USA Today. The impact of Tony Nominations tends to vary substantially across shows, although generally they tend to shift demand upwards.


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