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Maximum likelihood estimation of dependence parameter using ranked set sampling

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  • Modarres, Reza
  • Zheng, Gang

Abstract

We study the maximum likelihood estimation of the dependence parameter of a general bivariate distribution using ranked set sampling. We compare the Fisher information about the dependence parameter in ranked set samples and simple random samples. Results are applied to the bivariate normal and bivariate extreme value distributions. In ranked set sampling with unequal allocations, we select samples using maximal Fisher information in order statistics. We study the performance of the parametric bootstrap for bivariate ranked set samples.

Suggested Citation

  • Modarres, Reza & Zheng, Gang, 2004. "Maximum likelihood estimation of dependence parameter using ranked set sampling," Statistics & Probability Letters, Elsevier, vol. 68(3), pages 315-323, July.
  • Handle: RePEc:eee:stapro:v:68:y:2004:i:3:p:315-323
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    References listed on IDEAS

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    1. Z. Abo-Eleneen & H. Nagaraja, 2002. "Fisher Information in an Order Statistic and its Concomitant," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(3), pages 667-680, September.
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    Cited by:

    1. Manoj Chacko & P. Thomas, 2008. "Estimation of a parameter of Morgenstern type bivariate exponential distribution by ranked set sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 301-318, June.
    2. Amini, Morteza & Ahmadi, J., 2007. "Fisher information in record values and their concomitants about dependence and correlation parameters," Statistics & Probability Letters, Elsevier, vol. 77(10), pages 964-972, June.

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