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Optimality of ratio type estimation methods for population mean in the presence of missing data

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  • Shashi Bhushan
  • Abhay Pratap Pandey

Abstract

This article proposes various Searls-type ratio imputation methods (STRIM) on the lines of Ahmed et al. (2006). It is a well-known fact that the optimal ratio type estimator attains the MSE of regression estimator (or optimal difference estimator) but while using Searls-type transformation (STT) (Searls (1964)) this may not always happen. These STRIM are shown to perform better than the imputation procedures of Ahmed et al. (2006). The STRIM may even outperform the Searls type difference imputation methods (STDIM) proposed by us in our earlier work, Bhushan and Pandey (2016). This study is concluded with the numerical study along with the theoretical comparison.

Suggested Citation

  • Shashi Bhushan & Abhay Pratap Pandey, 2018. "Optimality of ratio type estimation methods for population mean in the presence of missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(11), pages 2576-2589, June.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:11:p:2576-2589
    DOI: 10.1080/03610926.2016.1167906
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    Cited by:

    1. Shashi Bhushan & Abhay Pratap Pandey, 2021. "Optimal imputation of the missing data using multi auxiliary information," Computational Statistics, Springer, vol. 36(1), pages 449-477, March.

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