Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-27-2020

Journal or Book Title

Journal of Statistical Computation and Simulation

Volume

90

Issue

6

First Page

1131

Last Page

1149

Research Focus Area(s)

​Operations Research

DOI

10.1080/00949655.2020.1718149

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.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation on January 27, 2020, available online: DOI: 10.1080/00949655.2020.1718149. Posted with permission.

Copyright Owner

Taylor & Francis Group, LLC

Language

en

File Format

application/pdf

Available for download on Wednesday, January 27, 2021

Published Version

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