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Imputation methods of missing data for estimating the population mean using simple random sampling with known correlation coefficient

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  • Amer Al-Omari
  • Carlos Bouza
  • Carmelo Herrera

Abstract

This paper considers three ratio estimators of the population mean using known correlation coefficient between the study and auxiliary variables in simple random sample when some sample observations are missing. The suggested estimators are compared with the estimators of Singh and Horn (Metrika 51:267–276, 2000 ), Singh and Deo (Stat Pap 44:555–579, 2003 ) and Kadilar and Cingi (Commun Stat Theory Methods 37:2226–2236, 2008 ). They are compared with other imputation estimators based on the mean or a ratio. It is found that the suggested estimators are approximately unbiased for the population mean. Also, it turns out that the suggested estimators perform well when compared with the other estimators considered in this study. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Amer Al-Omari & Carlos Bouza & Carmelo Herrera, 2013. "Imputation methods of missing data for estimating the population mean using simple random sampling with known correlation coefficient," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 353-365, January.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:1:p:353-365
    DOI: 10.1007/s11135-011-9522-1
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    References listed on IDEAS

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    1. H. Toutenburg & V. Srivastava & Shalabh, 2008. "Amputation versus imputation of missing values through ratio method in sample surveys," Statistical Papers, Springer, vol. 49(2), pages 237-247, April.
    2. S. González & M. Rueda & A. Arcos, 2008. "An improved estimator to analyse missing data," Statistical Papers, Springer, vol. 49(4), pages 791-796, October.
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