时空计数的近似线性INGARCH模型

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-03-16 DOI:10.1093/jrsssc/qlad018

Malte Jahn, C. Weiß, Hee-Young Kim

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引用次数: 3

摘要

现有的用于时空计数的整数值广义自回归条件异方差(INGARCH)模型不允许负参数和自相关值。利用近似线性的INGARCH模型，提出了用于无界计数建模的统一、灵活的时空INGARCH框架。这些模型将消极依赖与长期记忆相结合。它们很容易适应特殊的边缘特征或交叉依赖:当建模降水数据(降雨时数)时，我们考虑零通货膨胀，而对于云覆盖数据(okta计数)，我们处理缺失数据和额外的相互关联。与空间误差模型相关的联结关系表现出令人满意的性能。

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Approximately linear INGARCH models for spatio-temporal counts

Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow for negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified and flexible spatio-temporal (B)INGARCH framework for modelling unbounded (bounded) counts is proposed. These models combine negative dependencies with kinds of a long memory. They are easily adapted to special marginal features or cross-dependencies: When modelling precipitation data (counts of rainy hours), we account for zero-inflation, while for cloud-coverage data (counts of okta), we deal with missing data and additional cross-correlation. A copula related to the spatial error model shows an appealing performance.

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.