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首页> 外文期刊>The Annals of applied statistics >GENERALIZED EXTREME VALUE REGRESSION FOR BINARY RESPONSE DATA: AN APPLICATION TO B2B ELECTRONIC PAYMENTS SYSTEM ADOPTION
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GENERALIZED EXTREME VALUE REGRESSION FOR BINARY RESPONSE DATA: AN APPLICATION TO B2B ELECTRONIC PAYMENTS SYSTEM ADOPTION

机译：二元响应数据的广义极值回归：在B2B电子付款系统采用中的应用

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摘要

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 O's and l '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.
机译：在信息系统研究中，特别感兴趣的问题是解释和预测公司采用新技术的可能性，以使市场促销仅针对那些更可能采用该技术的公司。通常，观察到的“采用者”和“非采用者”数量之间存在显着差异，通常将其编码为二进制响应。对此类二进制响应数据进行建模所涉及的一个关键问题是在回归模型中适当选择链接函数。在本文中，我们介绍了一种新的灵活的偏斜链接函数，用于基于广义极值（GEV）分布对二进制响应数据进行建模。我们展示了拟议的GEV链接如何提供比现有的倾斜链接更灵活和改进的倾斜链接回归模型，尤其是在处理数据中观察到的O和l之间的不平衡时。通过仿真数据集和2005年《财富》 100强公司采用的电子支付系统的计费数据集，说明了所提出模型的灵活性。

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