dc.contributor.author |
Chakraborty, Niladri
|
|
dc.contributor.author |
Human, Schalk William
|
|
dc.contributor.author |
Balakrishnan, Narayanaswamy
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|
dc.date.accessioned |
2018-04-11T06:59:23Z |
|
dc.date.issued |
2018-03 |
|
dc.description.abstract |
Distribution-free control charts gained momentum in recent years as they are more efficient in detecting a shift when there is a lack of information regarding the underlying process distribution. However, a distribution-free control chart for monitoring the process location often requires information on the in-control process median. This is somewhat challenging because, in practice, any information on the location parameter might not be known in advance and estimation of the parameter is therefore required. In view of this, a time-weighted control chart, labelled as the Generally Weighted Moving Average (GWMA) exceedance (EX) chart (in short GWMA-EX chart), is proposed for detection of a shift in the unknown process location; this chart is based on exceedance statistic when there is no information available on the process distribution. An extensive performance analysis shows that the proposed GWMA-EX control chart is, in many cases, better than its contenders. |
en_ZA |
dc.description.department |
Statistics |
en_ZA |
dc.description.embargo |
2019-03-25 |
|
dc.description.librarian |
hj2018 |
en_ZA |
dc.description.sponsorship |
In part by the National Research Foundation of South Africa (Grant Number: 71199) and STATOMET, Department of Statistics, University of Pretoria, South Africa. |
en_ZA |
dc.description.uri |
http://www.tandfonline.com/loi/gscs20 |
en_ZA |
dc.identifier.citation |
Niladri Chakraborty, Schalk W. Human & Narayanaswamy Balakrishnan (2018) A generally weighted moving average exceedance chart, Journal of Statistical Computation and Simulation, 88:9, 1759-1781, DOI: 10.1080/00949655.2018.1447573. |
en_ZA |
dc.identifier.issn |
0094-9655 (print) |
|
dc.identifier.issn |
1563-5163 (online) |
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dc.identifier.other |
10.1080/00949655.2018.1447573 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/64482 |
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dc.language.iso |
en |
en_ZA |
dc.publisher |
Taylor and Francis |
en_ZA |
dc.rights |
© 2018 Informa UK Limited, trading as Taylor & Francis Group. This is an electronic version of an article published in Journal of Statistical Computation and Simulation, vol. 88, no. 9, pp. 1759-1781, 2018. doi : 10.1080/00949655.2018.1447573. Journal of Statistical Computation and Simulation is available online at : http://www.tandfonline.com/loi/gscs20. |
en_ZA |
dc.subject |
Generally weighted moving average (GWMA) |
en_ZA |
dc.subject |
Nonparametric control chart |
en_ZA |
dc.subject |
Monte Carlo simulation |
en_ZA |
dc.subject |
Average run-length |
en_ZA |
dc.subject |
Precedence statistic |
en_ZA |
dc.subject |
Exceedance statistic |
en_ZA |
dc.subject |
GWMA chart |
en_ZA |
dc.title |
A generally weighted moving average exceedance chart |
en_ZA |
dc.type |
Postprint Article |
en_ZA |