A change-point detection and clustering method in the recurrent-event context. Issue 6 (12th April 2020)

• Record Type: Journal Article
• Authors: Li, Qing; Yao, Kehui; Zhang, Xinyu
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 6(2020)
• Page Start: 1131
• Page End: 1149
• Publication Date: 2020-04-12
• Subjects: K-means -- maximum likelihood estimate -- non-homogeneous Poisson process -- parametric bootstrap -- piecewise-constant intensity; 62-04 -- 62-07 -- 62-PXX; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1718149
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Change-point detection in the context of recurrent-event is a valuable analysis tool for the identification of the intensity rate changes. It has been an interesting topic in many fields, such as medical studies, travel safety analysis, etc. If subgroups exist, clustering can be incorporated into the change-point detection to improve the quality of the results. This paper develops a new algorithm named Recurrent-K-means to detect the change-points of the intensity rates and identify clusters of objects with recurrent events. It also proposes a test-based method to perform a heuristic search in determining the number of underlying clusters. In this study, the objects are assumed to fall in several clusters while the objects in the same cluster share identical change-points. The event count for an object is assumed to be a non-homogeneous Poisson process with a piecewise-constant intensity function. The methodology estimates the change-point as well as the intensity rates before and after the change-point for each cluster. The methodology establishes a clustering analysis based on K-means algorithm but enhances the procedure to be model based. The simulation study shows that the methodology performs well in parameter estimation and determination of the number of clusters in different scenarios. The methodology is applied to the UK coal mining disaster data to show its possible role in shaping government regulations and improving coal industry safety.