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Shrinkage strategy in stratified random sample subject to measurement error

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  • Nkurunziza, Sévérien

Abstract

The empirical likelihood estimation approach has been used in statistical applications. In this paper, we consider a stratified random sample subject to measurement error and with this framework, we propose a shrinkage estimation strategy that improves the performance of the maximum empirical likelihood estimator (MELE). Further, we generalize some recent findings that demonstrate the superiority of the shrinkage strategy over the MELE. Monte Carlo simulation results corroborate the established theoretical findings.

Suggested Citation

  • Nkurunziza, Sévérien, 2011. "Shrinkage strategy in stratified random sample subject to measurement error," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 317-325, February.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:2:p:317-325
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    References listed on IDEAS

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    1. Ahmed, S. Ejaz & Hussein, Abdulkadir & Nkurunziza, Sévérien, 2010. "Robust inference strategy in the presence of measurement error," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 726-732, April.
    2. Ahmed, S. Ejaz & Volodin, Andrei I. & Volodin, Igor N., 2009. "High order approximation for the coverage probability by a confident set centered at the positive-part James-Stein estimator," Statistics & Probability Letters, Elsevier, vol. 79(17), pages 1823-1828, September.
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    Cited by:

    1. M. Arashi & B. Kibria & A. Tajadod, 2015. "On shrinkage estimators in matrix variate elliptical models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(1), pages 29-44, January.
    2. Fuqi Chen & Sévérien Nkurunziza, 2016. "A class of Stein-rules in multivariate regression model with structural changes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 83-102, March.

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