Hartley-Ross type variance estimators in simple random sampling

Kadılar, Cem; Çekim, Hatice Öncel

doi:10.1063/1.4992691

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Considering their estimators in ( 1 ) and biased in ( 2 ), Hartley–Ross type estimators are proposed by Kadilar and Cekim 15

Here

instead of

in ( 2 ) and

.…”

Section: Evaluation Of Estimators In Literaturementioning

confidence: 99%

“…Considering their estimators in () and biased in (), Hartley–Ross type estimators are proposed by Kadilar and Cekim 15

{s}_{KCj}={s}_j^2-B\left({s}_j^2\right),\kern.5em j=1,2,3,4.

Here

\left(\frac{M_{22}}{s_y^2{S}_x^2}-1\right)

instead of

{\lambda}_{22}^{\ast }

in () and

{M}_{pr}=\frac{1}{n-1}\sum \limits_{i=1}^n{\left({Y}_i-\overline{y}\right)}^p{\left({X}_i-\overline{y}\right)}^r

.…”

Section: Evaluation Of Estimators In Literaturementioning

confidence: 99%

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Modified unbiased estimators for population variance: An application for COVID‐19 deaths in Russia

Çekim

2022

Concurrency and Computation

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Summary The article deal with the class unbiased forms of the variance estimators using Hartley–Ross type in the simple random sampling. The mean squared error (since it is an unbiased estimator, variance is calculated) of the suggested, up to the first order of approximation, is derived. The proposed estimator using COVID‐19 data in Russia has been proven to be more efficient than the considered estimators under the conditions. Thus, it allows us to see the variance estimator that best predicts the change according to federal regions in Russia's number of COVID‐19 deaths. This study also provides an unbiased family of estimators that are more efficient than existing estimators, which estimate the variance of the total death number of COVID‐19 based on the daily new cases number. The most important difference of this article from other studies in the literature is that it is the first study to examine the variance of the COVID‐19 cumulative mortality value in terms of variance estimation using the simple random sampling method with the auxiliary variable.

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“…Considering their estimators in ( 1 ) and biased in ( 2 ), Hartley–Ross type estimators are proposed by Kadilar and Cekim 15

Here

instead of

in ( 2 ) and

.…”

Section: Evaluation Of Estimators In Literaturementioning

confidence: 99%

“…Considering their estimators in () and biased in (), Hartley–Ross type estimators are proposed by Kadilar and Cekim 15

{s}_{KCj}={s}_j^2-B\left({s}_j^2\right),\kern.5em j=1,2,3,4.

Here

\left(\frac{M_{22}}{s_y^2{S}_x^2}-1\right)

instead of

{\lambda}_{22}^{\ast }

in () and

{M}_{pr}=\frac{1}{n-1}\sum \limits_{i=1}^n{\left({Y}_i-\overline{y}\right)}^p{\left({X}_i-\overline{y}\right)}^r

.…”

Section: Evaluation Of Estimators In Literaturementioning

confidence: 99%

Modified unbiased estimators for population variance: An application for COVID‐19 deaths in Russia

Çekim

2022

Concurrency and Computation

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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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Hartley-Ross type variance estimators in simple random sampling

Cited by 2 publications

References 5 publications

Modified unbiased estimators for population variance: An application for COVID‐19 deaths in Russia

Modified unbiased estimators for population variance: An application for COVID‐19 deaths in Russia

ln-Type Variance Estimators in Simple Random Sampling

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