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December 2010 Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption
Xia Wang, Dipak K. Dey
Ann. Appl. Stat. 4(4): 2000-2023 (December 2010). DOI: 10.1214/10-AOAS354

Abstract

In the information system research, a question of particular interest is to interpret and to predict the probability of a firm to adopt a new technology such that market promotions are targeted to only those firms that were more likely to adopt the technology. Typically, there exists significant difference between the observed number of “adopters” and “nonadopters,” which is usually coded as binary response. A critical issue involved in modeling such binary response data is the appropriate choice of link functions in a regression model. In this paper we introduce a new flexible skewed link function for modeling binary response data based on the generalized extreme value (GEV) distribution. We show how the proposed GEV links provide more flexible and improved skewed link regression models than the existing skewed links, especially when dealing with imbalance between the observed number of 0’s and 1’s in a data. The flexibility of the proposed model is illustrated through simulated data sets and a billing data set of the electronic payments system adoption from a Fortune 100 company in 2005.

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Xia Wang. Dipak K. Dey. "Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption." Ann. Appl. Stat. 4 (4) 2000 - 2023, December 2010. https://doi.org/10.1214/10-AOAS354

Information

Published: December 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1220.62165
MathSciNet: MR2829944
Digital Object Identifier: 10.1214/10-AOAS354

Keywords: Generalized extreme value distribution , latent variable , Markov chain Monte Carlo , posterior distribution , skewness

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 4 • December 2010
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