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

Author

Listed:
  • Nidhi
  • B. V. S. Sisodia
  • Subedar Singh
  • Sanjay K. Singh

Abstract

Calibration estimation improves the precision of the estimates of population parameters by incorporating specified auxiliary information. A class of calibration estimators has been proposed for estimating the population mean by making use of a set of calibration constraints in stratified sampling. The estimator of variance of the proposed calibration estimator of the mean is derived using a lower level calibration approach. The idea is extended for stratified double sampling. A simulation study is used to evaluate the performances of the proposed estimators by comparing them with the similar estimators developed by Tracy, Singh and Arnab (2003) based on different sets of calibration constraints.

Suggested Citation

  • Nidhi & B. V. S. Sisodia & Subedar Singh & Sanjay K. Singh, 2017. "Calibration approach estimation of the mean in stratified sampling and stratified double sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(10), pages 4932-4942, May.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:10:p:4932-4942
    DOI: 10.1080/03610926.2015.1091083
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    Cited by:

    1. J. Al-Jararha & Mazen Sulaiman, 2020. "Horvitz-Thompson estimator based on the auxiliary variable," Statistics in Transition New Series, Polish Statistical Association, vol. 21(1), pages 37-53, March.
    2. Usman Shahzad & Ishfaq Ahmad & Fatimah Alshahrani & Ibrahim M. Almanjahie & Soofia Iftikhar, 2023. "Calibration-Based Mean Estimators under Stratified Median Ranked Set Sampling," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    3. Ahmed Audu & Rajesh Singh & Supriya Khare, 2021. "Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 125-142, June.

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