Journal Article10.1080/16843703.2004.11673073
Nonparametric Predictive Inference in Statistical Process Control
TL;DR: In this paper, a control chart based on the extrema of a sample of observations from the process is proposed, which is a generalisation of an existing nonparametric method, which controls a process using single observations.
read more
Abstract: Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric predictive inference. The proposed ‘extrema chart’, based on the extrema of a sample of observations from the process, is a generalisation of an existing nonparametric method, which controls a process using single observations. We examine the average run length (ARL) of both the one-sided and two-sided extrema chart, and a simulation study is presented to compare the extrema chart with the well known X¯ chart and CUSUM chart. The disadvantage of these charts is that when the process mean and variation of the in-control process have to be estimated, the ARL is biased. This is not an issue for the extrema chart, as no knowledge about the underlying distribution is required.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Nonparametric CUSUM and EWMA Control Charts for Detecting Mean Shifts
TL;DR: In this paper, two nonparametric analogs of the CUSUM and EWMA control charts based on the Wilcoxon rank-sum test are proposed for detecting process mean shifts.
134
On Nonparametric Predictive Inference and Objective Bayesianism
TL;DR: An overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A(n) and uses interval probability to quantify uncertainty, and a discussion of NPI and objective Bayesianism.
Rank-based EWMA procedure for sequentially detecting changes of process location and variability
Zhonghua Li,Min Xie,Maoyuan Zhou +2 more
TL;DR: This paper presents a study of a new procedure, which is based on integrating a powerful nonparametric test for the two-sample problem and EWMA control scheme to online sequential monitoring, and is quite robust to nonnormally distributed data.
46
Predictive inference for system reliability after common-cause component failures
TL;DR: The nonparametric predictive inference approach is presented for a basic scenario of a system consisting of only a single type of components and without consideration of failure behaviour over time, it provides many opportunities for more general modelling and inference.
43
References
•Book
Introduction to Statistical Quality Control
Douglas C. Montgomery
- 01 Jan 1985
TL;DR: In this article, the authors present a survey of statistical process control and capability analysis techniques for improving the quality of a business process in the modern business environment, using a variety of techniques.
8.2K
•Book
Statistical Quality Control
Eugene L. Grant,Richard S. Leavenworth +1 more
- 01 Mar 1996
TL;DR: This title is a substantial revision of one of the leading textbooks designed for the statistical quality control course taught in departments of industrial engineering, operations research and statistics and has incorporated key organizational changes in order to reflect recent trends in the field.
1K
Nonparametric Control Charts: An Overview and Some Results
TL;DR: An overview of the literature on nonparametric or distribution-free control charts for univariate variables data is presented and connections to some areas of active research are made, such as sequential analysis, that are relevant to process control.
377
Posterior Distribution of Percentiles: Bayes' Theorem for Sampling From a Population
TL;DR: In this article, it was shown that there are no countably additive exchangeable distributions on the space of observations which give ties probability 0 and for which a next observation is conditionally equally likely to fall in any of the open intervals between successive order statistics of a given sample.
261