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Spatial estimation and rescaled spatial bootstrap approach for finite population

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  • Ankur Biswas
  • Anil Rai
  • Tauqueer Ahmad
  • Prachi Misra Sahoo

Abstract

In this study, an attempt has been made to improve the sampling strategy incorporating spatial dependency at estimation stage considering usual aerial sampling scheme, such as simple random sampling, when the underlying population is finite and spatial in nature. Using the distances between spatial units, an improved method of estimation, viz. spatial estimation procedure, has been proposed for the estimation of finite population mean. Further, rescaled spatial bootstrap (RSB) methods have been proposed for approximately unbiased estimation of variance of the proposed spatial estimator (SE). The properties of the proposed SE and its corresponding RSB methods were studied empirically through simulation.

Suggested Citation

  • Ankur Biswas & Anil Rai & Tauqueer Ahmad & Prachi Misra Sahoo, 2017. "Spatial estimation and rescaled spatial bootstrap approach for finite population," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(1), pages 373-388, January.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:1:p:373-388
    DOI: 10.1080/03610926.2014.995820
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    Cited by:

    1. Bappa Saha & Ankur Biswas & Tauqueer Ahmad & Nobin Chandra Paul, 2024. "Geographically Weighted Regression-Based Model Calibration Estimation of Finite Population Total Under Geo-referenced Complex Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 793-811, December.

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