Efficient Ratio-Type Estimators of Finite Population Mean Based on Correlation Coefficient

Irfan, Muhammad; Javed, Maria; Zhang, Lin

doi:10.24200/sci.2017.4455

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Following the lines of Shabbir et al [20], a generalized estimator for the estimation of population mean is proposed using some traditional and nontraditional measures of an auxiliary variable. For more details of these nontraditional measures, see the works of Hettmansperger and McKean [21], Wang et al [22], and Irfan et al [23][24][25][26]:…”

Section: Suggested Family Of Estimatorsmentioning

confidence: 99%

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

Javed

Irfan

Bhatti

et al. 2021

Journal of Mathematics

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This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.

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Section: Suggested Family Of Estimatorsmentioning

confidence: 99%

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

Javed

Irfan

Bhatti

et al. 2021

Journal of Mathematics

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“…Several authors have used ratio, product and regression-type estimators to estimate population mean when both study and auxiliary variables are directly observable. For detail, see the following references: Kadilar and Cingi [2][3], Gupta and Shabbir [4], Grover and Kaur [5][6], Singh and Solanki [7], Haq and Shabbir [8], Shabbir et al [9], Ekpenyong and Enang [10], Khan et al [11], Solanki and Singh [12], Srisodaphol et al [13], Singh and Pal [14], Singh et al [15], Irfan et al [16][17], Javed et al [18] etc. This section gives a brief introduction of traditional estimators i.e.…”

Section: Traditional and Existing Exponential-type Estimatorsmentioning

confidence: 99%

Difference-Type-Exponential Estimators Based on Dual Auxiliary Information Under Simple Random Sampling

2020

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Auxiliary information plays a vital role at the selection and/or estimation stage to achieve the efficient estimates of the unknown population parameters. Dual use of auxiliary information, one the original and second the ranks of the auxiliary variable help to increase the efficiency of the estimators. In this article, we proposed and evaluated the performance of difference-type-exponential estimators based on dual auxiliary information for population mean under simple random sampling. Mathematical expressions for the bias and the mean squared error of the proposed estimators are obtained. Three real-life data sets and Monte Carlo simulation studies are carried out for illustration. The results of the empirical and the simulation studies, in terms of mean square errors and percentage relative efficiencies indicate that the proposed estimators perform better as compared to their counterparts.

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“…Singh et al [15] and Munoz et al [16]. Some other useful references are, Irfan et al [17], Abid et al. [18], Abid et al [19], Javed et al [20], Naz et al [21], Younis and Shabbir [22], Ahmed and Shabbir [23] and Nazir et al [24].…”

Section: Introductionmentioning

confidence: 99%

On use of subsampling of the non-respondents for estimation of distribution function

Shabbir

Gupta

2021

Scientia Iranica

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In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents.We use the Hansen and Hurwitz pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, and many well-known difference type estimators by using the criterion of MSE.

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Efficient Ratio-Type Estimators of Finite Population Mean Based on Correlation Coefficient

Cited by 4 publications

References 19 publications

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

Difference-Type-Exponential Estimators Based on Dual Auxiliary Information Under Simple Random Sampling

On use of subsampling of the non-respondents for estimation of distribution function

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