A spatial scan statistic for beta regression

Lima, Máx Sousa de; Santos, Vanessa Souza dos; Duczmal, Luiz Henrique; Souza, Diego da Silva

doi:10.1016/j.spasta.2016.09.003

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Similar to previous studies, in the scenarios with clusters very far away, specificity and PPV stay at high level 6,17,27,32 , so that the sensitivity and overall performance measures such as YDI and misclassification are of Figure 6. Detected clusters obtained using different parameters.…”

Section: Discussionsupporting

confidence: 80%

“…Subsequently, MWSs with the largest the MCHS-P and the MCS-P were selected as their own optimal parameters, respectively. The SSS's performance characteristics obtained using the MCHS-P, MCS-P, 50% MWS, and www.nature.com/scientificreports www.nature.com/scientificreports/ Gini coefficient were compared using the average values of 5 commonly used classic performance measures over replicas: sensitivity, specificity, positive predictive value (PPV), Youden's index (YDI) and misclassification 22,[26][27][28][29] . Additionally, we calculated the average values of the MCHS-P, the MCS-P and each classic performance measure for each MWS over replicas to evaluate the relationships of the MCHS-P and the MCS-P with the classic performance measures.…”

Section: Resultsmentioning

confidence: 99%

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Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic

Wang

Zhang

Yin

et al. 2020

Sci Rep

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The spatial scan statistic has been widely used to detect spatial clusters that are of common interest in many health-related problems. However, in most situations, different scan parameters, especially the maximum window size (MWS), result in obtaining different detected clusters. Although performance measures can select an optimal scan parameter, most of them depend on historical prior or true cluster information, which is usually unavailable in practical datasets. Currently, the Gini coefficient and the maximum clustering set-proportion statistic (McS-p) are used to select appropriate parameters without any prior information. However, the Gini coefficient may be unstable and select inappropriate parameters, especially in complex practical datasets, while the MCS-P may have unsatisfactory performance in spatial datasets with heterogeneous clusters. Based on the MCS-P, we proposed a new indicator, the maximum clustering heterogeneous set-proportion (MCHS-P). A simulation study of selecting the optimal MWS confirmed that in spatial datasets with heterogeneous clusters, the MWSs selected using the MCHS-P have much better performance than those selected using the MCS-P; moreover, higher heterogeneity led to a larger advantage of the MCHS-P, with up to 538% and 69.5% improvement in the Youden's index and misclassification in specific scenarios, respectively. Meanwhile, the MCHS-P maintains similar performance to that of the MCS-P in spatial datasets with homogeneous clusters. Furthermore, the MCHS-P has significant improvements over the Gini coefficient and the default 50% MWS, especially in datasets with clusters that are not far from each other. Two practical studies showed similar results to those obtained in the simulation study. In the case where there is no prior information about the true clusters or the heterogeneity between the clusters, the MCHS-P is recommended to select the MWS in order to accurately identify spatial clusters.

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Section: Discussionsupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic

Wang

Zhang

Yin

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…It seems that the use of parallel computation, Bayesian methods, and graph theory can help researchers to tackle this challenge. On the other hand, to the best of our knowledge, there is little work on detecting spatial clusters for proportional data (de Lima et al, 2016) and no parametric scan method to detect spatial clusters in the presence of heavy tailed and/or asymmetric spatial data. We believe that working with stable spatial models can be a way to improve scan statistics methods.…”

Section: Discussionmentioning

confidence: 99%

An up-to-date review of scan statistics

Abolhassani

Prates

2021

Statist. Surv.

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Scan statistics have been a very important and active area of statistical research in the past three decades. Detecting areas with a significant concentration of points is an important task in understanding the underlying phenomena in many fields such as: epidemiology, politics, crime analysis, zoology, etc. This study reviews how scan statistics have developed in the last three decades, the main concerns of researchers in scan statistics, and how researchers have approached these concerns.

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Statistical analysis of teleconsultations in the context of the COVID-19 epidemic

Sainte-Rose

Vaillant

2022

Communications in Statistics: Case Studies, Data Analysis and A

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A spatial scan statistic for beta regression

Cited by 4 publications

References 25 publications

Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic

Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic

An up-to-date review of scan statistics

Statistical analysis of teleconsultations in the context of the COVID-19 epidemic

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