Estimation of the population mean by successive use of an auxiliary variable in median ranked set sampling

Shahzad, Usman; Ahmad, Ishfaq; Oral, Evrim; Hanif, Muhammad; Almanjahie, Ibrahim M.

doi:10.1080/08898480.2020.1816703

Cited by 21 publications

(9 citation statements)

References 8 publications

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“…Singh and Nigam 4 discussed efficient method of estimating the finite population mean based on two auxiliary variables in the presence of non-response under stratified sampling. Shahzad et al 5 discussed estimation of the population mean by successive use of an auxiliary variable in median ranked set sampling. Singh et al 6 proposed some imputation methods to deal with the problems of missing data in two-occasion successive sampling.…”

Section: Introductionmentioning

confidence: 99%

A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

Ahmad

Ullah

Zahid

et al. 2023

Sci Rep

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This article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error (MSE) are derived up to first degree of approximation. From our generalized class of estimators, we obtained two improved estimators. The gain in second proposed estimator is more as compared to first estimator. Three real data sets and a simulation are accompanied to measure the performances of our generalized class of estimators. The MSE of our proposed estimators is minimum and consequently percentage relative efficiency is higher as compared to their existing counterparts. From the numerical outcomes it has been shown that the proposed estimators perform well as compared to all considered estimators in this study.

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Section: Introductionmentioning

confidence: 99%

A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

Ahmad

Ullah

Zahid

et al. 2023

Sci Rep

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“…Analysts have dedicated much attention to the assessment of not only population mean but also aggregate or total, and variance, see for example, Zamanzade and Vock, 1 Zaman and Bulut, 2,3 Zaman, 4,5 Shahzad et al 6,7 However, a lower level of consideration has been given to the assessment (estimation) of the population coefficient of variation (CV). The CV is generally applied in every field of life.…”

Section: Introductionmentioning

confidence: 99%

Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

Shahzad

Ahmad

Hanif

et al. 2022

Concurrency and Computation

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Assessment of coefficient of variation (CV) is of major importance in numerous examinations. However, the appearance of extreme observations raises concerns about the outcomes of CV estimates based on conventional moments. So, motivated by some recent developments in finite sampling theory, we propose some new estimators of CV based on the properties of linear moments (L-moments and Trimmed L-moments), which are highly robust whenever outliers or extreme observations appear in a dataset.The proposed estimators are initially established on the premise that the variable of interest is nonsensitive which deals with the subjects that do not embarrass respondents when asked about them explicitly. These estimators are also applied to situations where the variable of interest is associated with sensitive issues that cause measurement errors resulting from nonresponse or unreliable reporting where such issues can be mitigated by increasing respondent participation by scrambled response models which obscure the true value of the sensitive variable. Four models are considered for this article: additive, multiplicative, mixed, and combined additive-multiplicative models. Finally, in both nonsensitive and sensitive settings, real-life data sets are employed to undertake simulation-based analysis. In all cases, the proposed estimators considerably increased efficiency.

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“…The traditional regression estimator was examined by [6] under RSS. However, in the last few years, statisticians such as [7][8][9][10][11][12][13][14][15][16][17] turned their focus to the realm of RSS and suggested several modified and improved estimators to evaluate the population parameters by employing auxiliary information. This article investigates a modified class of estimators consisting of RSS for evaluating the population mean.…”

Section: Introductionmentioning

confidence: 99%

Modified Class of Estimators Using Ranked Set Sampling

et al. 2022

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The present article discusses the issue of population mean estimation in the ranked set sampling framework. A modified class of estimators is proffered and compared in the aspect of its efficacious performance with all salient conventional estimators existing to date. Some well-known existing estimators under RSS are recognized as the members of the proffered estimators for appropriately chosen characterizing scalars. The ascendancy of the proposed class of estimators regarding the conventional estimators has been shown through an extensive computational study utilizing some natural and artificially generated populations.

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Estimation of the population mean by successive use of an auxiliary variable in median ranked set sampling

Cited by 21 publications

References 8 publications

A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

Modified Class of Estimators Using Ranked Set Sampling

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