A study on the chain ratio-ratio-type exponential estimator for finite population variance

Singh, Housila P.; Pal, Surya K.; Yadav, Anita

doi:10.1080/03610926.2017.1321124

Cited by 16 publications

(11 citation statements)

References 6 publications

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“…From this result, we can conclude that using the ln -function in variance estimators improves the efficiency of the estimators. In the future, the proposed estimator can be extended to a family of estimators, such as in the studies of and Singh et al (2017). Also, different estimators of other sampling methods can be developed using the ln -function.…”

Section: Discussionmentioning

confidence: 99%

ln-Type Variance Estimators in Simple Random Sampling

Çekim

Kadılar

2020

Pak.j.stat.oper.res.

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Until now, various types of estimators have been used for estimating the population variance in simple random sampling studies, including ratio, product, regression and exponential-type estimators. In this article, we propose a family of -type estimators for the first time in the simple random sampling and show that they are more efficient than the other types of estimators under certain conditions obtained theoretically. Numerical illustrations and a simulation study support our findings in theory. In addition, it has been shown how to determine the optimal points in order to reach the minimum MSE values with the properties of the ln-type estimators in the different data sets.

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Section: Discussionmentioning

confidence: 99%

ln-Type Variance Estimators in Simple Random Sampling

Çekim

Kadılar

2020

Pak.j.stat.oper.res.

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“…x is known and when 2 y s in (2) is replaced with R t , then Singh et al [21] provided the chain ratio estimator as…”

Section: If the Population Variancementioning

confidence: 99%

“…Examining (1) and ( 6), Singh's et al [21] estimator provides a lower MSE than the unbiased estimator under the condition 1 C  . From (3) and ( 6), Singh's et al [13] estimator provides a lower MSE than the Isaki's [20] estimator under the condition 1.5 C  .…”

Section: If the Population Variancementioning

confidence: 99%

A new class of robust ratio estimators for finite population variance

Zaman

Bulut

2022

Scientia Iranica

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It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the mean square error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.

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“…Various types of estimators have been developed in the context of ratio, product and regression estimators for estimating population variance by using the known auxiliary information based on different conventional measures such as mean, median, quartiles, semi-interquartile range, semi-interquartile average, coefficient of skewness, coefficient of kurtosis etc. One can see the work of Isaki [3], Upadhyaya and Singh [4], Kadilar and Cingi [5], Subramani and Kumarapandiyan [6][7][8][9], Khan and Shabbir [10], Hussain and Shabbir [11], Zamanzade and Vock [12], Yaqub and Shabbir [13], Abid, Abbas and Riaz [14], Maqbool and Javaid [15], Adichwal, Sharma and Singh [16], Maji, Singh and Bandyopadhyay [17], Zamanzade and Wang [18], Zamanzade and Mahdizadeh [19], Singh, Pal and Yadav [20], Zamanzade, E and Wang [21], Hussain et al [22], Muneer et al [23], Mahdizadeh and Zamanzade [24], Abid et al [25] and the references cited therein.…”

Section: Introductionmentioning

confidence: 99%

An Improved and Robust Class of Variance Estimator

et al. 2020

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The ratio, product, and regression estimators are commonly constructed based on conventional measures such as mean, median, quartiles, semi-interquartile range, semiinterquartile average, coefficient of skewness, and coefficient of kurtosis. In case of the presence of outliers, these conventional measures lose their efficiency/performance ability and hence are of less efficient as compared to those measures which performed efficiently in the presence of outliers. This study offers an improved class of estimators for estimating the population variance using robust dispersion measures such as probability-weighted moments, Gini's, Downton's and Bickel and Lehmann measures of an auxiliary variable. Bias, mean square error, and minimum mean square error of the suggested class of estimators have been derived. Application with two natural data sets is also provided to explain the proposal for practical considerations. In addition, a robustness study is also carried out to evaluate the performance of the proposed estimators in the presence of outliers by using environmental protection data. The results reveal that the proposed estimators perform better than their competitors and are robust, not only in simple conditions but also in the presence of outliers.

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A study on the chain ratio-ratio-type exponential estimator for finite population variance

Cited by 16 publications

References 6 publications

ln-Type Variance Estimators in Simple Random Sampling

ln-Type Variance Estimators in Simple Random Sampling

A new class of robust ratio estimators for finite population variance

An Improved and Robust Class of Variance Estimator

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