Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption

Details Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption

2011-01-07

arxiv.org/abs/1101.1373v1

Annals of Applied Statistics 2010, Vol. 4, No. 4, 2000-2023

Statistics

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Published in at http://dx.doi.org/10.1214/10-AOAS354 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS354

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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