Extreme Ranked Repetitive Sampling Control Charts

Al‐Nasser, Amjad D.; Ciavolino, Enrico; Al-Omari, Amer Ibrahim

doi:10.1590/0101-7438.2020.040.00226135

Cited by 5 publications

(6 citation statements)

References 30 publications

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“…The MiniMax Ranked Set Sampling (MMRSS) proposed by Al‐Nasser and Al‐Omari 94 is an efficient and cost‐effective method to estimate the parameters of a target population. Unlike the other RSS methods, the MMRSS method uses unequal set sizes.…”

Section: Ranked Set Samplingmentioning

confidence: 99%

“…developed the Shewhart‐type mean control chart to monitor the process by employing the Quartile Pair Ranked Set Sampling (QPRSS) scheme. More recently, Al‐Nasser et al 123 . proposed the Shewhart ranked control chart using repetitive sampling within an ERSS scheme.…”

Section: Categorizing Literature Based On Type Of Control Charts Unde...mentioning

confidence: 99%

“…Tayyab et al 122 developed the Shewhart-type mean control chart to monitor the process by employing the Quartile Pair Ranked Set Sampling (QPRSS) scheme. More recently, Al-Nasser et al 123 proposed the Shewhart ranked control chart using repetitive sampling within an ERSS scheme. Hanandeh and Al-Nasser 124 designed a Shewhart-type mean control chart to monitor the process by employing the cost-effective MMRSS scheme.…”

Section: Shewhart-type Control Chartsmentioning

confidence: 99%

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A review of ranked set sampling and modified methods in designing control charts

Mohammadkhani

Amiri

Khoo

2023

Quality & Reliability Eng

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Statistical process monitoring (SPM) and designing control charts include measuring quality characteristics by considering the time and cost constraints. Ranked set sampling (RSS) is a very efficient and an inexpensive approach in obtaining a more representative sample when the actual measurement of the sampling units is too expensive or destructive, where it is easier to rank the observations. Since 1997, when the first paper on designing control charts based on the RSS scheme was presented, a significant number of papers have been published in this area. This paper provides a literature review on using the RSS schemes in designing control charts. Relevant papers are classified and analyzed to identify the research gaps and provide recommendations and directions for future studies.

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Section: Ranked Set Samplingmentioning

confidence: 99%

Section: Categorizing Literature Based On Type Of Control Charts Unde...mentioning

confidence: 99%

Section: Shewhart-type Control Chartsmentioning

confidence: 99%

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A review of ranked set sampling and modified methods in designing control charts

Mohammadkhani

Amiri

Khoo

2023

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Bani‐Mustafa et al (2011) proposed the folded RSS (FRSS) technique which has an advantage over the RSS and ERSS in terms of reducing the number of unused sampling units. Recently, Al‐Nasser and Al‐Omari (2018) introduced MiniMax RSS (MMRSS) which is an efficient mixed RSS and illustrated that MMRSS is more precise than SRS and requires fewer units to be ranked than RSS. For more details and applications about RSS, see, for example, Johnson et al (1993), Kaur et al (1995), Aragon et al (1999), Chen et al (2003), Bouza‐Herrera and Al‐Omari (2018), Al‐Omari (2011, 2012, 2013, 2014), Hanandeh and Al‐Nasser (2020, 2021), and references cited therein.…”

Section: Introductionmentioning

confidence: 99%

New mixed ranked set sampling variations

Hanandeh

Al‐Nasser

Al-Omari

2022

Stat

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Building efficient sampling procedures to provide more accurate results with small sample sizes is one of the main goals in sampling surveys. The ranked set sampling (RSS) is a well‐known procedure for selecting representative samples and improving parametric estimation by employing ranking on observations. In order to have a more efficient sampling procedure, new sampling procedures are proposed and investigated in this article. The main focus of this article is to enhance the mean estimation of the study population using the proposed RSS procedures. The performance of the proposed sampling procedures is compared with their competitors in RSS, double RSS (DRSS), extreme RSS (ERSS), and double extreme RSS (DERSS) by conducting simulation studies for numerous (symmetric and asymmetric) distributions. An application to a real dataset is also considered to exemplify the achievement of the proposals. Numerical simulations show that the new modified estimators are unbiased for the population mean for symmetric distributions and they outperform their competitors in most of the cases investigated in this article.

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“…Haq et al [16] suggested a new modification of the RSS called as paired double ranked set sampling. Al-Nasser et al [3] suggested extreme ranked repetitive sampling control charts and Haq et al [14] proposed mixed ranked set sampling method.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation in location-scale families using varied L ranked set sampling

Al-Omari

2021

RAIRO-Oper. Res.

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Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.

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Extreme Ranked Repetitive Sampling Control Charts

Cited by 5 publications

References 30 publications

A review of ranked set sampling and modified methods in designing control charts

A review of ranked set sampling and modified methods in designing control charts

New mixed ranked set sampling variations

Maximum likelihood estimation in location-scale families using varied L ranked set sampling

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