Semiparametric detection of changepoints in location, scale, and copula
Semiparametric detection of changepoints in location, scale, and copula
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DOI：
10.1002/sam.11622
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Gaurav Agarwal;I. Eckley;P. Fearnhead
中科院分区：
文献类型：
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作者：
Gaurav Agarwal;I. Eckley;P. Fearnhead
本文提出了一种检测单变量数据序列位置和规模变化点的新方法。所提出的方法假设数据属于位置尺度分布族，并以非参数方式估计相关密度。具体来说，该方法不需要了解数据序列分布的函数形式。因此，该方法可以检测许多分布中的变化点。我们还提出了一种新方法来检测多元序列位置的变化，使用边际和联结来捕获变量之间的依赖性，而不受边际分布的影响。通过模拟研究以及健康和金融领域的应用，将所提出的半参数方法的性能与其他竞争性非参数方法和高斯方法进行了对比。
This paper proposes a new method to detect changepoints in the location and scale of univariate data sequences. The proposed method assumes that the data belong to the location‐scale family of distributions and estimate the associated densities nonparametrically. Specifically, the approach does not require knowledge of the functional form of the distribution of the data sequence. As such, the approach can detect changepoints in many distributions. We also propose a new method to detect changes in the location of multivariate sequences, using the marginals and a copula to capture the dependence between variables without the influence of marginal distributions. The performance of the proposed semiparametric approach is contrasted against both other competing nonparametric and Gaussian methods, via simulation studies, as well as applications arising from health and finance.