Use of calibration constraints and linear moments for variance estimation under stratified adaptive cluster sampling

Shahzad, Usman; Ahmad, Ishfaq; Al‐Noor, Nadia H.; Troon, Benedict

doi:10.1007/s00500-022-07430-z

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Because of these benefits, the distribution has a promising potential for applications in a wide range of industries, such as biotechnological life testing, durability, and econometric data. For more readings see [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling for COVID 19 infected patient’s data in Kingdom of Saudi Arabia

Aldallal

Gemeay²,

Hussam³

et al. 2022

PLoS ONE

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The objective of this study is to construct a new distribution known as the weighted Burr–Hatke distribution (WBHD). The PDF and CDF of the WBHD are derived in a closed form. Moments, incomplete moments, and the quantile function of the proposed distribution are derived mathematically. Eleven estimate techniques for estimating the distribution parameters are discussed, and numerical simulations are utilised to evaluate the various approaches using partial and overall rankings. According to the findings of this study, it is recommended that the maximum product of spacing (MPSE) estimator of the WBHD is the best estimator according to overall rank table. The actuarial measurements were derived to the suggested distribution. By contrasting the WBHD with other competitive distributions using two different actual data sets collected from the COVID-19 mortality rates, we show the importance and flexibility of the WBHD.

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Section: Introductionmentioning

confidence: 99%

Statistical modeling for COVID 19 infected patient’s data in Kingdom of Saudi Arabia

Aldallal

Gemeay²,

Hussam³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

An enhanced estimator of finite population variance using two auxiliary variables under simple random sampling

Ahmad,

Adichwal,

Aamir

et al. 2023

Sci Rep

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In this article, we have suggested a new improved estimator for estimation of finite population variance under simple random sampling. We use two auxiliary variables to improve the efficiency of estimator. The numerical expressions for the bias and mean square error are derived up to the first order approximation. To evaluate the efficiency of the new estimator, we conduct a numerical study using four real data sets and a simulation study. The result shows that the suggested estimator has a minimum mean square error and higher percentage relative efficiency as compared to all the existing estimators. These findings demonstrate the significance of our suggested estimator and highlight its potential applications in various fields. Theoretical and numerical analyses show that our suggested estimator outperforms all existing estimators in terms of efficiency. This demonstrates the practical value of incorporating auxiliary variables into the estimation process and the potential for future research in this area.

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Use of calibration constraints and linear moments for variance estimation under stratified adaptive cluster sampling

Cited by 2 publications

References 26 publications

Statistical modeling for COVID 19 infected patient’s data in Kingdom of Saudi Arabia

Statistical modeling for COVID 19 infected patient’s data in Kingdom of Saudi Arabia

An enhanced estimator of finite population variance using two auxiliary variables under simple random sampling

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