Investigating spatial scan statistics for multivariate functional data

被引：4

作者：

Frevent, Camille ^{[1
,2
]}

Ahmed, Mohamed-Salem ^{[1
,3
]}

Dabo-Niang, Sophie ^{[2
,4
]}

Genin, Michael ^{[1
]}

机构：

[1] Univ Lille, CHU Lille, METRICS Evaluat Technol Sante & Prat Med ULR 2694, F-59000 Lille, France

[2] INRIA Lille Nord Europe, MODAL Team, Lille, France

[3] Alicante SARL, Lesquin, France

[4] Univ Lille, CNRS, Lab Paul Painleve UMR 8524, F-59000 Lille, France

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS | 2023年 / 72卷 / 02期

关键词：

Hotelling T-2-test; MANOVA; multivariate functional data; spatial clusters; spatial scan statistics; Wilcoxon rank-sum test; GENERALIZED LINEAR-MODELS; AIR-POLLUTION; EVENT DETECTION; REGRESSION; MORTALITY; PRECIPITATION; CLUSTERS; AMBIENT;

D O I：

10.1093/jrsssc/qlad017

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In environmental surveillance, cluster detection of environmental black spots is of major interest due to the adverse health effects of pollutants, as well as their known synergistic effect. Thus, this paper introduces three new spatial scan statistics for multivariate functional data, applicable for detecting clusters of abnormal air pollutants concentrations measured spatially at a very fine scale in northern France in October 2021 taking into account their correlations. Mathematically, our methodology is derived from a functional multivariate analysis of variance, an adaptation of the Hotelling T-2-test statistic, and a multivariate extension of the Wilcoxon test statistic. The approaches were evaluated in a simulation study and then applied to the air pollution dataset.

引用

页码：450 / 475

页数：26

共 127 条

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