A decision theoretic approach to change point estimation for binomial CUSUM control charts

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105675

Title:	A decision theoretic approach to change point estimation for binomial CUSUM control charts
Authors:	江村剛志;Emura, Takeshi;Ho, Yi-Ting
Contributors:	理學院統計研究所
Keywords:	62L0;Attribute control chart;Binomials;Confidence intervals;Control charts;Decision theory;Economic models;Estimators;Jewelry;Maximum likelihood estimators;np-chart;parametric bootstrap;Sequential analysis;SPRT;Statistical process control
Date:	2016-04-02
Issue Date:	2026-04-23 12:47:05 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Philadelphia: Taylor & Francis
Abstract:	摘要： Detecting when the process has changed is a classical problem in sequential analysis and is an important practical issue in statistical process control. This article is concerned about the binomial cumulative sum (CUSUM) control chart, which is extensively applied to industrial process control, health care, public health surveillance, and other fields. For the binomial CUSUM, a maximum likelihood estimator has been proposed to estimate the change point. In our article, following a decision theoretic approach, we develop a new estimator that aims to improve the existing methods. For interval estimation, we propose a parametric bootstrap procedure to construct the confidence set of the change point. We compare our proposed method with the maximum likelihood estimator and Page's last zero estimator in terms of mean squared error by simulations. We find that the proposed method gives more unbiased and robust results than the existing procedures under various parameter designs. We analyze jewelry manufacturing data for illustration. 出版者： Philadelphia: Taylor & Francis 出版日期： 2016-04-02 出處： Sequential Analysis, 2016-04, Vol.35 (2), p.238-253 版權： Copyright © Taylor & Francis Group, LLC 2016 版權： Copyright © Taylor & Francis Group, LLC 識別號： ISSN: 0747-4946 識別號： EISSN: 1532-4176 識別號： DOI: 10.1080/07474946.2016.1165543
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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