A Regression Type Estimator with Two Auxiliary Variables for Two-Phase Sampling

Hamad, Naqvi; Hanif, Muhammad; Haider, Najeeb

doi:10.4236/ojs.2013.32010

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…In sampling and quality control literature there are many instances where auxiliary information has been used for improvements and new developments. In the literature of sampling techniques, Hanif et al [18], Hamad et al [19] and Awan and Shabbir [20] have developed new estimators by using auxiliary information. In quality control there are many cases where control charts have been improved by using correlated auxiliary variables; for example, Shabbir and Awan [2] proposed an efficient control chart to monitor process mean wherein charting statistic is based on two auxiliary variables.…”

Section: Designing Of Ewma-did Control Chartmentioning

confidence: 99%

An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information

2018

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Among the Statistical Process Control (SPC) techniques, control charts are considered to be high weight-age due to their effectiveness in process variation. As the Shewhart's charts are not that active in monitoring small and moderate process variations, the statisticians have been making efforts to improve the performance of the control chart by introducing several techniques within the tool. These techniques consist of experimenting with different estimators, different sampling selection techniques, and mixed methodologies. The proposed chart is one of the examples of a mixed chart technique that has shown its efficiency in monitoring small variations better than any of the existing techniques in the specific situation of auxiliary information. To show and compare its performance, average run length (ARL) tables and ARL curves have been presented in the article. An industrial example has also been included to show the practical application of the proposed chart in a real scenario.

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Section: Designing Of Ewma-did Control Chartmentioning

confidence: 99%

An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information

2018

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“…One of them is the optimal choice of the secondoccasion sampling design in order to minimize the variance of the estimator (Arnab,[3]). Hamad et al [10] uses two auxiliary variables for the estimator of the total in a twophase sampling design. Subsampling of nonrespondents and corresponding estimators is also a case of estimation under a two-phase sampling design (Okafor and Lee, [16]), under a three-phase sampling design (Hidiriglou and Estevao [11]).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the current population total for a four-phase sampling design

Chadyšas

Krapavickaitė

2016

NAMC

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The combined ratio-type estimators of the finite population total and their variances in the case of sample rotation for two-phase and four-phase sampling schemes are constructed in the paper. Combined estimators of the finite population total without and with the use of auxiliary information known from the previous survey are built. Two types of sampling design are used for sample selection in each of the phases: simple random sampling without replacement and successive sampling without replacement with probabilities proportional to size. A simulation study, based on the real data, is performed, and the accuracy of the estimators proposed is compared.

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“…The estimation procedures using auxiliary information by Watwon are the initial work in the field of auxiliary information. Efficient estimators can be obtained by using auxiliary variables, for example, we can see the works of Awan and Shabbir, Hanif et al , Hamad et al , and references therein. Kadilar and Cingi and Awan and Shabbir developed new estimators using two auxiliary variables.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Shewhart-Type Control Chart to Monitor Moderate Size Shifts in the Process Mean in Phase II

Shabbir

Awan

2015

Qual. Reliab. Engng. Int.

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According to Shewhart, control charts are not very sensitive to small and moderate size process shifts that is why those are less likely to be effective in Phase II. So to monitor small or moderate size process shifts in Phase II, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are considered as alternate of Shewhart control charts. In this paper, a Shewhart-type control chart is proposed by using difference-in-difference estimator in order to detect moderate size shifts in process mean in Phase II. The performance of the proposed control chart is studied for known and unknown cases separately through a detailed simulation study. For the unknown case, instead of using reference samples of small sizes, large size reference sample(s) is used as we can see in some of nonparametric control chart articles. In an illustrative example, the proposed control charts are constructed for both known and unknown cases along with Shewhart X -chart, classical EWMA, and CUSUM control charts. In this application, the proposed chart is found comprehensively better than not only Shewhart X-chart but also EWMA and CUSUM control charts. By comparing average run length, the proposed control chart is found always better than Shewhart X -chart and in general better than classical EWMA and CUSUM control charts when we have relatively higher values of correlation coefficients and detection of the moderate shifts in the process mean is concerned.

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A Regression Type Estimator with Two Auxiliary Variables for Two-Phase Sampling

Cited by 15 publications

References 10 publications

An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information

An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information

Estimation of the current population total for a four-phase sampling design

An Efficient Shewhart-Type Control Chart to Monitor Moderate Size Shifts in the Process Mean in Phase II

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