分组网络泊松自回归模型

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-01-01 DOI:10.5705/ss.202022.0040
Yuxin Tao, Dongyu Li, Xiaoyue Niu
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引用次数: 1

摘要

虽然多元泊松自回归模型是流行的拟合计数时间序列数据,统计推断是相当具有挑战性的。网络泊松自回归(NPAR)模型通过将网络信息纳入依赖结构来降低推理复杂性,其中每个个体的响应可以用其滞后值和相邻个体的平均效应来解释。然而,NPAR模型强烈假设所有个体都是同质的,并且有一个共同的自回归系数。在此,我们提出了一个分组网络泊松自回归(GNPAR)模型,该模型将个体分为不同的组,使用组特定参数来描述异构节点行为。给出了GNPAR模型的平稳性和遍历性,并研究了极大似然估计的渐近性质。我们开发了一种期望最大化算法来估计未知的组标签,并使用模拟研究了我们的估计过程的有限样本性能。我们分析了芝加哥警方调查停止报告的数据,并在芝加哥不同的社区发现了不同的依赖模式,这可能有助于未来的犯罪预防。
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Grouped Network Poisson Autoregressive Model
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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