Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

Shahzad, Usman; Hanif, Muhammad; Sajjad, Irsa; Anas, Malik Muhammad

doi:10.24200/sci.2020.54423.3744

Cited by 24 publications

(23 citation statements)

References 29 publications

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“…The reasonable utilization of the supplementary information helps in expanding the exactness of an estimator both at the structuring stage as well as at the estimation stage, see [21][22][23][24][25][26][27][28][29][30][31][32][33]. In social, monetary, and regular studies, the total supplementary information is frequently accessible to the inspected outline.…”

Section: Methodsmentioning

confidence: 99%

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme

Shahzad

Ahmad²,

Almanjahie³

et al. 2022

PLoS ONE

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Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.

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Section: Methodsmentioning

confidence: 99%

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme

Shahzad

Ahmad²,

Almanjahie³

et al. 2022

PLoS ONE

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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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“…Such sort of linear relationship allows researchers to use auxiliary variable X for improved estimation of any parameter of study variable Y . For more discussion on auxiliary information, interested readers may refer to Koyuncu [1], Al-Omari [2], Zaman [3,4], Naz et al [5,6], and Shahzad et al [7,8]. An alternative method for the situations in which an abundance of auxiliary information is available is ranked set sampling due to McIntyre [9].…”

Section: Introductionmentioning

confidence: 99%

L-Moments and calibration based variance estimators under double stratified random sampling scheme: an application of covid-19 pandemic

et al. 2021

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The presence of extreme events gives rise to outrageous results regarding population parameters and their estimates using traditional moments. Traditional moments are usually influenced by extreme observations. In this paper, we propose some new calibration estimators under the L-Moments scheme for variance which is one of the most important population parameters. Some suitable calibration constraints under double stratified random sampling are also defined for these estimators. Our proposed estimators based on L-Moments are relatively more robust in presence of extreme values. The empirical efficiency of proposed estimators is also calculated through simulation. Covid-19 pandemic data from January 22, 2020, to August 23, 2020, is considered for simulation study.

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Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

Cited by 24 publications

References 29 publications

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme

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

L-Moments and calibration based variance estimators under double stratified random sampling scheme: an application of covid-19 pandemic

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