Exponential ratio and product type estimators of finite population mean

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

doi:10.1080/09720510.2015.1040249

Cited by 16 publications

(7 citation statements)

References 6 publications

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“…where, P 0 , P 1 are denoted by existing estimators and P pro1(h) , P pro2(h) , P pro3(h) are denoted by developed estimators utilized in the PRE's formula shown in (23). In this study, consider the population for summarizing through the simulation steps.…”

Section: Simulation Studymentioning

confidence: 99%

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Estimation of Finite Population Variance Under Stratified Sampling Technique

Yasmeen

Noor‐ul‐Amin

2021

JRSS

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The efficiency of the study variable can be improved by incorporating the information from the known auxiliary variables. Usually two techniques ratio and regression estimation are used with the help of auxiliary information in different approaches to acquire the high precision of the estimators. Considering the very heterogeneous population to get the size of the sample it may be originating impossible to get a sufficiently accurate and precise estimate by taking the simple random sampling technique from the complete population. Occasionally taking sample issue may differ significantly in different part of the entire population. For example, under study population consists of people living in apartments, own homes, hospitals and prisons or people living in plain regions and hill regions so in such situations the stratified sampling is one of the most commonly used approach to get a representative sample in survey sampling from different cross units of the population. The present study is set out on the recommendation of generalized variance estimators for finite population variance incorporating stratified sampling scheme with the information of single and two transformed auxiliary variables. The expressions of bias and mean square error (MSE) are obtained for the advised exponential type estimators. The conditions are obtained for which the anticipated estimators are better than the usual estimator. An empirical and simulation study is conducted to prove the superiority of the recommended estimator.

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Section: Simulation Studymentioning

confidence: 99%

“…Step 3: Using the values found in step 2, the values of PRE is computed by (23) and reported in Table 1.…”

Section: Simulation Studymentioning

confidence: 99%

Estimation of Finite Population Variance Under Stratified Sampling Technique

Yasmeen

Noor‐ul‐Amin

2021

JRSS

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“…The minimum MSE of the proposed two classes of estimators that is, Ŷpj and Ŷprj will be more efficient than the Yasmeen et al 44 estimator if:…”

Section: Comparison With Yasmeen Et Al Ratio Estimatormentioning

confidence: 99%

“…Yasmeen et al 44 proposed a ratio-cum-exponential estimator based on information of dual auxiliary variables for estimation of the population mean Y under SRSWOR as follows:…”

Section: Yasmeen Et Al Estimatormentioning

confidence: 99%

On enhanced exponential‐cum‐ratio estimators using robust measures of location

Gulzar

Latif

Abid

et al. 2021

Concurrency and Computation

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Many studies are mainly concerned with the estimation of finite population mean and the well-known preferences for it are ratio estimators. This article offers a remedy for improvement in the estimation of a study variable, based on supplementary information related to dual auxiliary variables in the context of simple random sampling without replacement scheme. We proposed two new general classes of exponential-cum-ratio estimators to estimate a finite population mean utilizing dual auxiliary variables with suitable combinations of the conventional and nonconventional measures. The expression for the mean square error (MSE) and theoretical conditions for proposed classes have been obtained for evaluation purposes. The performance of the proposed classes has been compared with existing estimators in terms of MSE. It is revealed that proposed estimators are more efficient than usual and existing estimators considered in this article. The simulation and robustness studies are also part of this article. Moreover, a variety of real data sets is also considered for an empirical study to support the theoretical results.

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“…In the process of conducting sample surveys, researchers normally use ratio-type estimators when there is a need to estimate the unknown population parameters of the study variable by means of known population parameters of a correlated auxiliary variable(s). An eye view of literature on the estimation of population mean under simple random sampling without replacement (SRSWOR) with proper information on two auxiliary variables shows prominent studies of Abu-Dayyeh et al [1], Kadilar and Cingi [2], Singh and Tailor [3], Lu and Yan [4], Lu et al [5], Vishwakarma and Kumar [6], Sharma and Singh [7], Yasmeen et al [8], Lu [9], Muneer et al [10], and Shabbir and Gupta [11].…”

Section: Introductionmentioning

confidence: 99%

Hartley-Ross Type Unbiased Estimators of Population Mean Using Two Auxiliary Variables

2018

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In survey sampling, it is a well-established phenomenon that the e ciency of estimators increases with proper information on auxiliary variable(s). Keeping this fact in mind, the information on two auxiliary variables was utilized to propose a family of Hartley-Ross type unbiased estimators for estimating population mean under simple random sampling without replacement. Minimum variance of the new estimators was derived up to the rst degree of approximation. Three real datasets were used to verify the e cient performance of the new family in comparison to the usual unbiased, Hartley and Ross, and other competing estimators.

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Exponential ratio and product type estimators of finite population mean

Cited by 16 publications

References 6 publications

Estimation of Finite Population Variance Under Stratified Sampling Technique

Estimation of Finite Population Variance Under Stratified Sampling Technique

On enhanced exponential‐cum‐ratio estimators using robust measures of location

Hartley-Ross Type Unbiased Estimators of Population Mean Using Two Auxiliary Variables

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