Exponential Estimators of Finite Population Variance Using Transformed Auxiliary Variables

Yasmeen, Uzma; Noor‐ul‐Amin, Muhammad; Hanif, Muhammad

doi:10.1007/s40010-017-0410-5

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…For proper treatment, a doctor must have a detailed realization of differences in human hypertension and fever. Many authors suggested different types of estimators for variance including Das [1], Prasad and Singh [2], Swain and Mishra [3], Ahmad and Shabbir [4], Kadilar and Cingi [5], Subramani and Kumarapandiyan [6], Singh and Solanki [7], Yadav et al [8], Singh et al [9], Yadav and Kadilar [10], Adichwal et al [11,12], Yasmeen et al [13], Zaman [14,15], Zaman and Bulut [16], Zaman et al [17,18], Bhushan et al [19], Shahzad et al [20], Ahmad et al [21], Ozturk [22], Ozturk and Demirel [23], Frey and Feeman [24], Zamanzade and Vock [25], Mahdizadeh and Zamanzade [26,27], Bhushan et al [28], Bhushan et al [29], Zaman and Bulut [30,31], Yadav and Zaman [32], Zaman and Kadilar [33], Zaman and Dunder [34] and Singh and Khalid [35]. We requisite to estimate population parameters for numerous variables in multidisciplinary surveys, such as house lengths, income level, and the number of unemployed people, in socio-economic surveys.…”

Section: Introductionmentioning

confidence: 99%

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

et al. 2022

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In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.

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Section: Introductionmentioning

confidence: 99%

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

et al. 2022

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“…Recently, Maqbool and Javaid (2017) using tri-mean and semi quartile average of auxiliary variable, estimated the population variance of study variable. Yasmeen et al (2018) proposed the Exponential type estimators of finite population variance using transformed auxiliary variables and shown through numerical example that their proposed estimators are better than the competing estimators of population variance. Etebong (2018) suggested a modified ratio type estimator of population variance using auxiliary information and showed improvement over other estimators both theoretically and empirically.…”

Section: Introductionmentioning

confidence: 99%

Searching efficient estimator of population variance using tri-mean and third quartile of auxiliary variable

Yadav

Sharma

Mishra

2019

IJBDA

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This paper concerns with the estimation of population variance of study variable using tri-mean and third quartile of the auxiliary variable. In this study, the sampling properties, bias and mean squared error of the proposed estimator are demonstrated. The justification of the performance of the proposed estimator under SRSWOR has been made with reference to the competing estimators of population variance, the sample variance, Isaki (1983) estimator, the estimator due to Upadhyaya and Singh (1999), Kadilar and Cingi (2006) estimators, Subramani and Kumarapandiyan (2012) estimators, Khan and Shabbir (2013) estimator and Maqbool and Javaid (2017) estimator of population variance. Based on data provided by Murthy (1967), it has been demonstrated that the proposed estimator has shown a significant improvement over all competing estimators of population variance.

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Combination of memory type ratio and product estimators under extended EWMA statistic with application to wheat production

Zahid,

Noor-ul-Amin,

Khan

et al. 2023

Sci Rep

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The extended exponential weighted moving average (EEWMA) statistic is a memory type statistic that uses past observations along with the current information for the estimation of a population parameter to improve the efficiency of the estimators. This study utilized the EEWMA statistic to estimate the population mean with a suitable auxiliary variable. The ratio and product estimators are proposed for the surveys that are time-based by using current information along with that information. The approximate mean square errors are computed for the proposed memory type estimators and mathematical comparison is discussed to demonstrate the efficiency of the estimator. The simulation study was carried out to evaluate the performance of the proposed memory type estimators. It can be seen from the results that the efficiency of the estimator enhances by utilizing the current sample as well as past information. A real-life example is presented to illustrate the usage of proposed estimators.

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Exponential Estimators of Finite Population Variance Using Transformed Auxiliary Variables

Cited by 7 publications

References 11 publications

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

Searching efficient estimator of population variance using tri-mean and third quartile of auxiliary variable

Combination of memory type ratio and product estimators under extended EWMA statistic with application to wheat production

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