Count network autoregression

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Mirko Armillotta, Konstantinos Fokianos
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引用次数: 0

Abstract

We consider network autoregressive models for count data with a non-random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.

Abstract Image

计数网络自回归
我们考虑了具有非随机邻域结构的计数数据的网络自回归模型。我们在方法论上的主要贡献是提出了保证此类模型稳定性和有效统计推断的条件。我们考虑了网络维度固定和增加的两种情况,并证明准似然法推断提供了一致且渐近正态分布的估计值。文章还附有模拟结果和一个数据示例。
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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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