Published since April 1, 2001

Stochastics and Quality Control

ISSN: 2367-2404

Editor-in-chief: George P. Yanev

Founded by: Elart von Collani

Editorial Board: Justin Chimka, Boyan Dimitrov, Miguel González, Raj Govindaraju, Olgierd Hryniewicz, Vladimir K. Kaishev, Magdi S. Moustafa, S.P. Mukherjee, Ken Nishina, Abdur Rahim, Erwin M. Saniga

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About this journal

Objective
Stochastics and Quality Control is devoted to all fields related to reliability and quality control in economy, finance, manufacturing, production and inventory. It primarily publishes research articles, but survey articles on a topical area inside the scope are considered, too.

The journal pays special attention to the interdisciplinary character of the topic. Not only mathematicians, but also scientists, economists as well as scholars from other disciplines contribute papers. The methods and results published thus are of interest to various applications in real life.

Topics

• Applied Stochastic Processes
• Branching Processes: Theory and Applications
• Reliability
• Ordered Data Analysis
• Statistical Methodology
• Business Management
• Economy and Finance

Article formats
Research and survey articles

Information on submission process

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Up-to-date research on economic quality control
Interdisciplinary approach to the topic
Worldwide-known editors and reviewers
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History

History
Economic Quality Control (now, Stochastics and Quality Control) has emerged from the identically named Newsletter of the “Würzburg Research Group on Quality Control” which was founded in 1986 by Bernhard F. Arnolds, Elart von Collani and John Sheil. Until 2009 the journal was published in cooperation with Heldermann Verlag.　

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Volume 37 Issue 2

December 2022

Publicly Available December 1, 2022

Frontmatter

Page range: i-iv

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October 27, 2022

The 𝑛𝑝-Chart with 3-𝜎 Limits and the ARL-Unbiased 𝑛𝑝-Chart Revisited

Manuel Cabral Morais, Philipp Wittenberg, Camila Jeppesen Cruz

Page range: 107-116

Abstract

In the statistical process control literature, counts of nonconforming items are frequently assumed to be independent and have a binomial distribution with parameters ( n , p ) (n,p) , where 𝑛 and 𝑝 represent the fixed sample size and the fraction nonconforming. In this paper, the traditional n ⁢ p np -chart with 3-𝜎 control limits is reexamined. We show that, even if its lower control limit is positive and we are dealing with a small target value p 0 p_{0} of the fraction nonconforming ( p ) (p) , this chart average run length (ARL) function achieves a maximum to the left of p 0 p_{0} . Moreover, the in-control ARL of this popular chart is also shown to vary considerably with the fixed sample size 𝑛. We also look closely at the ARL function of the ARL-unbiased n ⁢ p np -chart proposed by Morais [An ARL-unbiased n ⁢ p np -chart, Econ. Qual. Control 31 (2016), 1, 11–21], which attains a pre-specified maximum value in the in-control situation. This chart triggers a signal at sample 𝑡 with probability one if the observed number of nonconforming items, x t x_{t} , is beyond the lower and upper control limits (𝐿 and 𝑈), probability γ L \gamma_{L} (resp. γ U \gamma_{U} ) if x t x_{t} coincides with 𝐿 (resp. 𝑈). A graphical display for the ARL-unbiased n ⁢ p np -chart is proposed, taking advantage of the qcc package for the statistical software R. Furthermore, as far as we have investigated, its control limits can be obtained using three different search algorithms; their computation times are thoroughly compared.

November 25, 2022

General Independent Competing Risks for Maintenance Analysis

Makram Krit

Page range: 117-126

Abstract

Repairable systems are submitted to corrective maintenance and condition-based preventive maintenance actions. Condition-based preventive maintenance occurs at times which are determined according to the results of inspections and degradation or operation controls. The generalization of the models suggested makes it possible to integrate the dependence between corrective and preventive maintenances. In order to take into account this dependency and the possibility of imperfect maintenances, generalized competing risks models have been presented in Doyen and Gaudoin (2006). In this study, we revise the general case in which the potential times to next corrective and preventive maintenance are independent conditionally to the past of the maintenance process. We address the identifiability issue and we find a result similar to that of Zhou, Lu, Shi and Cheng (2018) for usual competing risks. We propose realistic models with exponential risks and derive their likelihood functions.

November 23, 2022

On Normal-Laplace Stochastic Volatility Model

Shiji Kavungal, Rahul Thekkedath

Page range: 127-136

Abstract

This paper analyses a stochastic volatility model generated by first order normal-Laplace autoregressive process. The model parameters are estimated by the generalized method of moments. A simulation experiment is carried out to check the performance of the estimates. Finally, a real data analysis is provided to illustrate the practical utility of the proposed model and show that it captures the stylized factors of the financial return series.

October 28, 2022

Robust Optimization of an Imperfect Process when the Mean and Variance are Jointly Monitored under Dependent Multiple Assignable Causes

Ali Salmasnia, Samrad Jafarian-Namin, Behnam Abdzadeh

Page range: 137-151

Abstract

Imperfect processes experience fault productions over time due to specific causes. Integrating the statistical process control, maintenance policy, and economic production quantity has led to more favorable results for the imperfect processes in literature. When monitoring a process, multiple assignable causes (ACs) may shift it to an out-of-control state. As indicated recently, if the interdependency of ACs is neglected, the total cost will be underestimated. Moreover, the mean and variance can simultaneously be affected by the occurrence of ACs. A non-central chi-square (NCS) chart was suggested for its decent performance against X-R chart in detecting the process disturbances and lowering quality loss cost. Besides, the increased occurrence rate of ACs over time leads to higher quality and maintenance costs. Employing a non-uniform sampling (NUS) scheme can significantly reduce costs. In the literature of modeling for imperfect processes under multiple ACs, all input parameters have always been fixed. The effectiveness of the models depends somewhat on the accurate estimates of these parameters. In reality, the estimation of parameters may be associated with uncertainty. For the first time, a robust design approach is proposed for designing NCS chart by considering the interval estimation of uncertain parameters. A particle swarm optimization (PSO) algorithm is used to present solutions. The proposed model is investigated through a real numerical example.

November 2, 2022

Estimation and Confidence Intervals of Modified Process Capability Index Using Robust Measure of Variability

Mahendra Saha, Sanku Dey

Page range: 153-164

Abstract

The process capability index (PCI), denoted by 𝐼, is a well-known characteristic in quality control analysis. Using Gini’s mean difference, we construct a new PCI, I G I_{G} say, assuming the two-parameter Weibull distribution (WD). In order to estimate the proposed I G I_{G} when the process follows the WD, we use five classical methods of estimation and compare the performance of the obtained estimators with respect to their mean squared errors (MSEs) through a simulation study. Confidence intervals for the proposed PCI are constructed based on five bootstrap confidence intervals (BCIs) methods. Monte Carlo simulation study has been carried out to compare the performance of these five BCIs in terms of average widths and coverage probabilities. Finally, three real data sets from electronic and food industries are employed for illustrating the effectiveness of the proposed study. All these data sets show that the width of bias-corrected accelerated bootstrap interval is minimum among all other considered BCIs.

August 3, 2022

Cumulative Entropy and Income Analysis

N. Unnikrishnan Nair, B. Vineshkumar

Page range: 165-179

Abstract

In the present work, we investigate the applications of cumulative entropy as a tool in income analysis. The relationships the cumulative entropy has with income gap ratio, Lorenz curve, Gini index, Bonferroni curve and Zenga curve are provided. Applications of the results to real data are illustrated.

Volume 37 (2022)

Volume 36 (2021)

Volume 35 (2020)

Volume 34 (2019)

Volume 33 (2018)

Volume 32 (2017)

Volume 31 (2016)

Volume 30 (2015)

Volume 29 (2014)

Volume 28 (2013)

Volume 27 (2012)

Volume 26 (2011)

Volume 25 (2010)

Volume 24 (2009)

Volume 23 (2008)

Volume 22 (2007)

Volume 21 (2006)

Volume 20 (2005)

Volume 19 (2004)

Volume 18 (2003)

Volume 17 (2002)

Volume 16 (2001)

Cite Score	1.0
European Reference Index for the Humanities
SCImago Journal Rank	0.401
Source Normalized Impact per Paper	0.851

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Editorial

Editor-in-Chief

George P. Yanev
School of Mathematical and Statistical Sciences
The University of Texas Rio Grande Valley
1201 West University Drive
Edinburg, Texas 78539-2999
USA
Email: george.yanev@utrgv.edu

Founding Editor

Elart von Collani
Stochastikon GmbH
Schießhausstr. 15
97072 Würzburg
Germany
Email: collani@stochastikon.com

Associate Editors

Justin R. Chimka
Department of Industrial Engineering
University of Arkansas
4207 Bell Engineering Center
Fayetteville, AR 72701
USA
Email: jchimka@uark.edu

Boyan Dimitrov
Science and Mathematics Department
Kettering University
1700 West Third Avenue
Flint, MI 48504-4898
USA
Email: bdimitro@kettering.edu

Miguel González
Department of Mathematics
University of Extremadura
Avda. de Elvas, s/n
06071 Badajoz
Spain
Email: mvelasco@unex.es

Raj Govindaraj
Institute of Information Sciences & Technology
Massey University
Palmerston North
New Zealand
Email: k.govindaraju@massey.ac.nz

Olgierd Hryniewicz
Systems Research Institute
Polish Academy of Sciences
Newelska 6
01-447 Warszawa
Poland
Email: hryniewi@ibspan.waw.pl

Vladimir K. Kaishev
Faculty of Actuarial Science and Insurance
Cass Business School, City University London
106 Bunhill Row
London EC1Y 8TZ
UK
Email: v.kaishev@city.ac.uk

Magdi Moustafa
The American University
P.O.Box 2511
Cairo 11511, Egypt
Email: mmostafa@aucegypt.edu

S.P. Mukherjee
Department of Statistics
University College of Science
35 Ballygunge Circular Rd.
Calcutta 700 019, India

Email: prof.mukherjee@gmail.com

Ken Nishina
Nagoya Institute of Technology
Gokiso-cho, Showa-ku
Nagoya, Aichi, 466-8555
Japan
Email: nishina@nitech.ac.jp

Erwin Saniga
Department of Business Administration
University of Delaware
Newark, Delaware 19716
USA
Email: sanigae@be.udel.edu

(Deutsch)

Online ISSN: 2367-2404
Print ISSN: 2367-2390
Type: Journal
Language: English
Publisher: De Gruyter
First published: April 1, 2001
Publication Frequency: 2 Issues per Year
Audience: mathematicians, scientists, economists as well as scholars from other disciplines