Cpss: an R package for change-point detection by sample-splitting methods. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Wang, Guanghui; Zou, Changliang
• Is Part Of: Journal of quality technology. Volume 55:Issue 1(2023)
• Page Start: 61
• Page End: 74
• Publication Date: 2023-01-01
• Subjects: change-point detection -- cross-validation -- data-driven -- sample-splitting -- statistical process control; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2022.2035284
• Languages: English
• ISSNs: 0022-4065

Abstract

Abstract: Change-point detection is a popular statistical method for Phase I analysis in statistical process control. The cpss package has been developed to provide users with multiple choices of change-point searching algorithms for a variety of frequently considered parametric change-point models, including the univariate and multivariate mean and/or (co)variance change models, changes in linear models and generalized linear models, and change models in exponential families. In particular, it integrates the recently proposed COPSS criterion to determine the number of change-points in a data-driven fashion that avoids selecting or specifying additional tuning parameters in existing approaches. Hence it is more convenient to use in practical applications. In addition, the cpss package brings great possibilities to handle user-customized change-point models.