A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY
Anagh Chattopadhyay, Soudeep Deb
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引用次数: 0

Abstract

It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a new spatio-temporal model to deal with this problem in a binary setting, with an interesting application related to the COVID-19 pandemic, a phenomena that depends on both spatial proximity and temporal auto-correlation. Our model is defined through a hierarchical structure for the latent variable, which corresponds to the probit-link function. The mean of the latent variable in the proposed model is designed to capture the trend and the seasonal pattern as well as the lagged effects of relevant regressors. The covariance structure of the model is defined as an additive combination of a zero-mean spatio-temporally correlated process and a white noise process. The parameters associated with the space-time process enable us to analyze the effect of proximity of two points with respect to space or time and its influence on the overall process. For estimation and prediction, we adopt a complete Bayesian framework along with suitable prior specifications and utilize the concepts of Gibbs sampling. Using the county-level data from the state of New York, we show that the proposed methodology provides superior performance than benchmark techniques. We also use our model to devise a novel mechanism for predictive clustering which can be leveraged to develop localized policies.

Abstract Image

二元数据时空模型及其在分析 COVID-19 传播方向中的应用
分析和预测作为分类变量的连续现象的水平通常是人们最感兴趣的问题。在本文中,我们提出了一种新的时空模型来处理二元设置中的这一问题,其有趣的应用与 COVID-19 大流行有关,这种现象既取决于空间邻近性,也取决于时间自相关性。我们的模型是通过潜变量的分层结构定义的,与 probit 链接函数相对应。拟议模型中潜变量的均值旨在捕捉趋势和季节模式以及相关回归因子的滞后效应。模型的协方差结构被定义为零均值时空相关过程和白噪声过程的加法组合。与时空过程相关的参数使我们能够分析两点在空间或时间上的接近程度及其对整个过程的影响。在估计和预测方面,我们采用了完整的贝叶斯框架和适当的先验规范,并利用了吉布斯抽样的概念。通过使用纽约州的县级数据,我们证明了所提出的方法比基准技术具有更优越的性能。我们还利用我们的模型设计了一种新颖的预测聚类机制,可用于制定本地化政策。
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来源期刊
Asta-Advances in Statistical Analysis
Asta-Advances in Statistical Analysis 数学-统计学与概率论
CiteScore
2.20
自引率
14.30%
发文量
39
审稿时长
>12 weeks
期刊介绍: AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.
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