An adaptive variable-parameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process
Sabahno, Hamed LU
; Castagliola, Philippe
and Amiri, Amirhossein
(2020)
In Journal of Statistical Computation and Simulation
90(8).
p.1430-1465
- Abstract
Due to advances in technology, sampling procedures and short lag times between successive sampling, autocorrelation among the measured data has become common in most applications. Neglecting autocorrelation leads to a poor false alarm performance. In the current paper, the effect of the autocorrelation on the performance of a variable-parameters multivariate single control chart is investigated in the case of the simultaneous monitoring of the mean and variability. At first, formulas for the sample mean and variability of a multivariate autoregressive-moving average process are derived. Then, a variable-parameters single control chart is developed for the simultaneous monitoring of the mean vector and the covariance matrix of an... (More)
Due to advances in technology, sampling procedures and short lag times between successive sampling, autocorrelation among the measured data has become common in most applications. Neglecting autocorrelation leads to a poor false alarm performance. In the current paper, the effect of the autocorrelation on the performance of a variable-parameters multivariate single control chart is investigated in the case of the simultaneous monitoring of the mean and variability. At first, formulas for the sample mean and variability of a multivariate autoregressive-moving average process are derived. Then, a variable-parameters single control chart is developed for the simultaneous monitoring of the mean vector and the covariance matrix of an autocorrelated multivariate normal process. Next, the performance of the proposed control chart is evaluated by using eight performance measures based on a dedicated Markov chain model. Finally, by presenting an illustrative example, the application of the proposed scheme is demonstrated in practice.
(Less)
- author
- Sabahno, Hamed
LU
; Castagliola, Philippe
and Amiri, Amirhossein
- publishing date
- 2020-05-23
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- adaptive control chart, ATS, Autocorrelation, Markov chain, multiple performance measures, multivariate geometric progression, multivariate normal process, simultaneous monitoring scheme, variable-parameters scheme, VARMA
- in
- Journal of Statistical Computation and Simulation
- volume
- 90
- issue
- 8
- pages
- 36 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85080141190
- ISSN
- 0094-9655
- DOI
- 10.1080/00949655.2020.1730373
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
- id
- 7dd34e78-1729-443d-a7fc-36fd79e06416
- date added to LUP
- 2025-03-20 12:17:31
- date last changed
- 2025-04-04 14:53:03
@article{7dd34e78-1729-443d-a7fc-36fd79e06416,
abstract = {{<p>Due to advances in technology, sampling procedures and short lag times between successive sampling, autocorrelation among the measured data has become common in most applications. Neglecting autocorrelation leads to a poor false alarm performance. In the current paper, the effect of the autocorrelation on the performance of a variable-parameters multivariate single control chart is investigated in the case of the simultaneous monitoring of the mean and variability. At first, formulas for the sample mean and variability of a multivariate autoregressive-moving average process are derived. Then, a variable-parameters single control chart is developed for the simultaneous monitoring of the mean vector and the covariance matrix of an autocorrelated multivariate normal process. Next, the performance of the proposed control chart is evaluated by using eight performance measures based on a dedicated Markov chain model. Finally, by presenting an illustrative example, the application of the proposed scheme is demonstrated in practice.</p>}},
author = {{Sabahno, Hamed and Castagliola, Philippe and Amiri, Amirhossein}},
issn = {{0094-9655}},
keywords = {{adaptive control chart; ATS; Autocorrelation; Markov chain; multiple performance measures; multivariate geometric progression; multivariate normal process; simultaneous monitoring scheme; variable-parameters scheme; VARMA}},
language = {{eng}},
month = {{05}},
number = {{8}},
pages = {{1430--1465}},
publisher = {{Taylor & Francis}},
series = {{Journal of Statistical Computation and Simulation}},
title = {{An adaptive variable-parameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process}},
url = {{http://dx.doi.org/10.1080/00949655.2020.1730373}},
doi = {{10.1080/00949655.2020.1730373}},
volume = {{90}},
year = {{2020}},
}