Calibration approach estimation of the mean in stratified sampling and stratified double sampling

In this paper, a new improved calibration estimator for the population mean in a stratified sampling was proposed using two auxiliary variables. A simulation study was carried out to evaluate the performance and efficiency of the proposed estimator with respect to three estimators considered in the literature for estimating the population mean in a stratified sampling using two auxiliary variables. The results showed that the new estimator proved to be more efficient than the three existing estimators considered.

Section: Introductionmentioning

confidence: 99%

Improved calibration estimation of population mean in stratified sampling using two auxiliary variables

Oladugba,

Babatunde

2024

“…Several calibration estimators for the population mean in a stratified sampling using different parameters of one auxiliary variable in the calibration constraints have been proposed in the literature (see Tracey, Singh and Arnab (2003), Rao, Tekabu and Khan (2016), Koyuncu and Kadilar (2016), Sisodia, Singh and Singh (2017), Alam, Singh and Shabbir (2019), Garg and Pachori (2019), Alam and Shabbir (2020), Babatunde et al ( 2023), Oladugba et al (2023) etc.). Several calibration estimators were arrived at by modifying existing estimators (see , Garg and Pachori (2019)).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

Statistics in Transition new series (SiTns) is an international journal published jointly by the Polish Statistical Association (PTS) and Statistics Poland, on a quarterly basis (during 1993-2006 it was issued twice and since 2006 three times a year). Also, it has extended its scope of interest beyond its originally primary focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy through embracing questions related to systemic transformations of and within the national statistical systems, world-wide.The SiTns seeks contributors that address the full range of problems involved in data production, data dissemination and utilization, providing international community of statisticians and users -including researchers, teachers, policy makers and the general public -with a platform for exchange of ideas and for sharing best practices in all areas of the development of statistics.Accordingly, articles dealing with any topics of statistics and its advancement -as either a scientific domain (new research and data analysis methods) or as a domain of informational infrastructure of the economy, society and the state -are appropriate for Statistics in Transition new series.Demonstration of the role played by statistical research and data in economic growth and social progress (both locally and globally), including better-informed decisions and greater participation of citizens, are of particular interest.Each paper submitted by prospective authors are peer reviewed by internationally recognized experts, who are guided in their decisions about the publication by criteria of originality and overall quality, including its content and form, and of potential interest to readers (esp. professionals).

“…Deville and Sarndal (1992) defined it as a procedure of minimizing a distance function subject to calibration constraints. In this direction, various calibration estimators for various sampling schemes for estimating the population parameters by using different calibration constraints based on the available auxiliary information have been developed by Singh (2003), Kim et al (2007), Singh and Arnab (2011), Kadilar (2013 and2016), Mouhamed et al (2015), Clement and Enang (2017), Nidhi et al (2017), Garg and Pachori (2019, 2020, Alka et al (2021), Khare et al (2022), Pachori et al (2022), Kumar et al (2023), etc.…”

Section: Introductionmentioning

confidence: 99%

“…

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al ( 2003) together its ratio-type estimators, as well as Nidhi et al (2017) and Khare et al (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators.

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mentioning

confidence: 99%

Ratio-type estimator of the population mean in stratified sampling based on the calibration approach

Pachori,

Garg

2024

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al. (2003) together its ratio-type estimators, as well as Nidhi et al. (2017) and Khare et al. (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators.