Cumulative Mean Function of Public Posting Episodes in the Online Media with Regard to User’s Digital Traces: Limited Data on publications Dates and Profile Data

V. F. Stoliarova, A. L. Tulupyev
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Abstract

The problem of behavior associated risk assessment arises in some areas that are closely related with a person’s behavior. The data for this problem is often incomplete, what brings the problem into the soft computing framework. In the paper the cumulative mean function is considered as the characteristic, reflecting the behavior pattern. Two approaches are presented for the modelling of this function in the context of gamma Poisson mathematical model of behavior: the nonparametric approach and regression-based one. Various observed and unobserved individual characteristics can be taken into account in the regression model. The approaches are illustrated with the data on public posting in the online media.
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基于用户数字轨迹的网络媒体公开发帖集的累积平均函数:有限的发布日期和个人资料数据
行为相关风险评估的问题出现在一些与人的行为密切相关的领域。这个问题的数据通常是不完整的,这将问题带入了软计算框架。本文以累积均值函数为特征,反映了行为模式。在行为的伽玛泊松数学模型的背景下,提出了两种方法来建模这个函数:非参数方法和基于回归的方法。在回归模型中可以考虑到各种观察到的和未观察到的个体特征。通过网络媒体公开发帖的数据说明了这些方法。
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