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

Ahmad, Sohaib; Hussain, Sardar; Ullah, Kalim; Zahid, Erum; Aamir, Muhammad; Shabbir, Javid; Ahmad, Zubair; Alshanbari, Huda M.; Alajlan, Wejdan

doi:10.1371/journal.pone.0276540

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Singh and Nigam [22] discussed about the ratio cum product-type exponential estimator in double sampling for stratifcation of the fnite population mean. Ahmad et al [23] proposed an improved ratio-in-regression type variance estimator based on dual use of auxiliary variables under simple random sampling.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

Ahmad

Zahid

Shabbir

et al. 2023

Mathematical Problems in Engineering

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In some situations, the population of interest differs significantly in size, for example, in a medical study, the number of patients having a specific disease and the size of health units may vary. Similarly, in a survey related to the income of a household, the household may have a different number of siblings, and then in such situations, we use probability proportional to size sampling. In this article, we have proposed an improved class of estimators for the estimation of population mean on the basis of probability proportional to size (PPS) sampling, using two auxiliary variables. The mathematical expressions of the bias and mean square error (MSE) are derived up to the first order of approximation. Four real datasets and a simulation study are conducted to assess the efficiency of the improved class of estimators. It is found from the real datasets and a simulation study, that the proposed generalized class of estimators produced better results in terms of minimum MSE and higher PRE, as related to other considered estimators. An empirical study is given to support the theoretical results. The theoretical study also demonstrates that the proposed generalized class of estimators outperforms the existing estimators.

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Section: Introductionmentioning

confidence: 99%

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

Ahmad

Zahid

Shabbir

et al. 2023

Mathematical Problems in Engineering

Self Cite

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“…Mahdizadeh and Zamanzade 11 proposed an interval estimation of the population mean in ranked set sampling. Ahmad et al 12 recommended a new improved generalized class of estimators for population distribution function using the auxiliary variable under simple random sampling. Muhammad et al 13 suggested an enhanced ratio-type estimator for finite population mean using the auxiliary variable in simple random sampling.…”

Section: Introductionmentioning

confidence: 99%

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

Ahmad,

Shabbir,

Zahid

et al. 2023

Science Progress

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This article aims to suggest a new generalized class of estimators based on probability proportional to size sampling using two auxiliary variables. The numerical expressions for the bias and mean squared error (MSE) are derived up to the first order of approximation. Four actual data sets are used to examine the performances of a new improved generalized class of estimators. From the results of real data sets, it is examined that the suggested estimator gives the minimum MSE and the percentage relative efficiency is higher than all existing estimators, which shows the importance of the new generalized class of estimators. To check the strength and generalizability of our proposed class of estimators, a simulation study is also accompanied. The consequence of the simulation study shows the worth of newly found proposed class estimators. Overall, we get to the conclusion that the proposed estimator outperforms as compared to all other estimators taken into account in this study.

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“…Additionally, Singh et al (2015) developed a chain regression-type estimator for estimating population mean through successive sampling on two occasions, incorporating multi-auxiliary variables. Ahmad et al (2022) put forth an enhanced variance estimator. This novel approach leverages dual auxiliary information to enhance the robustness and generalization of the proposed estimator.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Variance Estimation Methods in Two-Phase Sampling: A Focus on Regression-cum-Exponential Estimators with Multiple Auxiliaries

Asghar,

Sanaullah,

Khan

et al. 2023

BBE

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In this study, we introduce a regression-cum-exponential estimator designed for estimating population variance. Specifically, we focus on the estimation of unknown population variance in a two-phase sampling setup, considering the use of multiple auxiliary variables. We derive and discuss various cases pertaining to this estimation framework. Additionally, we compare the asymptotic properties of existing approaches with those of our proposed estimator. This allows us to assess the performance and efficiency of the different methods. Finally, we conduct a simulation study to evaluate the performance of our proposed estimator in finite samples, specifically utilizing multi-auxiliary variables. This empirical analysis provides insights into the practical effectiveness of the estimators.

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

Cited by 5 publications

References 36 publications

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

Comparative Analysis of Variance Estimation Methods in Two-Phase Sampling: A Focus on Regression-cum-Exponential Estimators with Multiple Auxiliaries

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