A New Exponential Type Estimator for the Population Mean in Simple Random Sampling

Kadılar, Gamze Özel

doi:10.22237/jmasm/1478002380

Cited by 23 publications

(8 citation statements)

References 11 publications

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“…Table 2 represents the control chart multipliers 𝐾 , defined in Equation (15) for correlations 𝜌 = 0.20, 0.50, 0.80 and 0.95 and 𝑛 = 10. Mathematics 2020, 8, x FOR PEER REVIEW 10 of 21 defined in Equation (18). Table 2 represents the control chart multipliers 𝐾 , defined in Equation (15) for correlations 𝜌 = 0.20, 0.50, 0.80 and 0.95 and 𝑛 = 10.…”

Section: Performance Evaluation Of Median Control Charts In Phase Imentioning

confidence: 99%

“…The auxiliary information, i, enhances the precision of estimator(s) or improves the sampling plan. There are several studies in literature of survey sampling available for the use of auxiliary information and it has been conclusively shown that the use of auxiliary information enhances the accuracy of estimators for population mean [18], population median [19] or population variance [20].…”

Section: Introductionmentioning

confidence: 99%

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On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

2020

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A control chart is often used to monitor the industrial or services processes to improve the quality of the products. Mostly, the monitoring of location parameters, both in Phase I and Phase II, is done using a mean control chart with the assumption that the process is free from outliers or the estimators are correctly estimated from in-control samples. Generally, there are question marks about such kind of narratives. The performance of the mean chart is highly affected in the presence of outliers. Therefore, the median chart is an attractive alternative to the mean chart in this situation. The control charts are usually implemented in two phases: Phase I (retrospective) and Phase II (prospective/monitoring). The efficiency of any control chart in Phase II depends on the accuracy of control limits obtained from Phase I. The current study focuses on the Phase I analysis of location parameters using median control charts. We examined the performance of different auxiliary information-based median control charts and compared the results with the usual median chart. Standardized variance and relative efficacy are used as performance measures to evaluate the efficiency of median estimators. Moreover, the probability to signal measure is used to evaluate the performance of proposed control charts to detect any potential changes in the process. The results revealed that the proposed auxiliary information based median control charts perform better in Phase I analysis. In addition, a practical illustration of an industrial scenario demonstrated the significance of the proposed control charts, in which the monitoring of concrete compressive strength is emphasized.

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Section: Performance Evaluation Of Median Control Charts In Phase Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

2020

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“…Estimators Srivastava (1967), Reddy (1974), Nangsue (2009), Kadilar (2016), Soponviwatkul and Lawson (2017), Ijaz and Ali (2018 Bahl and Tuteja (1991 Kadilar and Cingi (2003 Sisodia and Dwivedi (1981), Upadhyaya and Singh (1999), Singh and Tailor (2003), Singh et al (2004), Al-Omari et al (2009, Yan and Tian (2010), Kumarpandiyan (2012a, 2012b), Jeelani et al (2013), Jerajuddin and Kishun (2016) estimators International Journal of Mathematical, Engineering and Management Sciences Vol. 4, No.…”

Section: Efficiency Conditionmentioning

confidence: 99%

Improved Estimators for Estimating Average Yield Using Auxiliary Variable

Yadav¹,

Dixit²,

Dungana³

et al. 2019

Int J Math, Eng, Manag Sci

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In this paper, we consider the improved estimation of average production of peppermint at block level of Barabanki district of Uttar Pradesh State (India). We suggest certain estimators for population-mean. Here, population refers to production population as study variable and auxiliary-variable refers to Area of field. We study the sampling properties naming bias and MSE of estimators, which are presently proposed by us in the paper. We compare our proposed estimators with other ones existing in literature. For the support of the theoretical findings, we carry out a numerical study for the natural population on primary data collected from Banikodar Block of Barabanki District situated in Uttar Pradesh State.

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“…Grover and Kaur [8] suggested a generalized class of exponential-ratio-type exponential estimators using the conventional measures for mean estimation. Kadilar [9] discussed a new exponential-type estimator for mean estimation. Irfan et al [10] suggested a generalized ratio-exponential-type estimator using the conventional measures.…”

Section: Introductionmentioning

confidence: 99%

On Using the Conventional and Nonconventional Measures of the Auxiliary Variable for Mean Estimation

Shabbir

Gupta

Onyango

2021

Mathematical Problems in Engineering

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In this paper, we propose an improved new class of exponential-ratio-type estimators for estimating the finite population mean using the conventional and the nonconventional measures of the auxiliary variable. Expressions for the bias and MSE are obtained under large sample approximation. Both simulation and numerical studies are conducted to validate the theoretical findings. Use of the conventional and the nonconventional measures of the auxiliary variable is very common in survey research, but we observe that this does not add much value in many of the estimators except for our proposed class of estimators.

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A New Exponential Type Estimator for the Population Mean in Simple Random Sampling

Cited by 23 publications

References 11 publications

On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

Improved Estimators for Estimating Average Yield Using Auxiliary Variable

On Using the Conventional and Nonconventional Measures of the Auxiliary Variable for Mean Estimation

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