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Sequential order statistics with an order statistics prior

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  • Burkschat, M.
  • Kamps, U.
  • Kateri, M.
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    Abstract

    In the model of sequential order statistics, prior distributions are considered for the model parameters, which, for example, describe increasing load put on remaining components. Gamma priors are examined as well as priors out of a class of extended truncated Erlang distributions (ETED), which is introduced along with some properties. The choice of independent priors in both set-ups leads to respective independent, conjugate posterior distributions for the model parameters of sequential order statistics. Since, in practical applications, the model parameters will often be increasingly ordered, a multivariate prior is applied being the joint distribution of common ETED-order statistics. Whatever baseline distribution of the sequential order statistics is chosen, the joint posterior distribution turns out to be a Weinman multivariate exponential distribution. Posterior moments are given explicitly, and HPD credible sets for the model parameters are stated.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 101 (2010)
    Issue (Month): 8 (September)
    Pages: 1826-1836

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    Handle: RePEc:eee:jmvana:v:101:y:2010:i:8:p:1826-1836

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    Related research

    Keywords: ETED Gamma distribution Erlang distribution Independent gamma priors Weinman multivariate exponential distribution;

    References

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    1. Khaledi, Baha-Eldin & Kochar, Subhash, 2005. "Dependence orderings for generalized order statistics," Statistics & Probability Letters, Elsevier, vol. 73(4), pages 357-367, July.
    2. Belzunce, Félix & Mercader, José A. & Ruiz, José M., 2003. "Multivariate aging properties of epoch times of nonhomogeneous processes," Journal of Multivariate Analysis, Elsevier, vol. 84(2), pages 335-350, February.
    3. Erhard Cramer & Udo Kamps, 2003. "Marginal distributions of sequential and generalized order statistics," Metrika, Springer, vol. 58(3), pages 293-310, December.
    4. Cramer, Erhard, 2004. "Logconcavity and unimodality of progressively censored order statistics," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 83-90, June.
    5. Hu, Taizhong & Zhuang, Weiwei, 2005. "A note on stochastic comparisons of generalized order statistics," Statistics & Probability Letters, Elsevier, vol. 72(2), pages 163-170, April.
    6. Balakrishnan, N. & Beutner, E. & Kamps, U., 2008. "Order restricted inference for sequential k-out-of-n systems," Journal of Multivariate Analysis, Elsevier, vol. 99(7), pages 1489-1502, August.
    7. Erhard Cramer & Udo Kamps, 2001. "Estimation with Sequential Order Statistics from Exponential Distributions," Annals of the Institute of Statistical Mathematics, Springer, vol. 53(2), pages 307-324, June.
    8. Zografos, K. & Nadarajah, S., 2005. "Expressions for Rényi and Shannon entropies for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 71(1), pages 71-84, January.
    9. Burkschat, M., 2009. "Multivariate dependence of spacings of generalized order statistics," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1093-1106, July.
    10. Chen, Huaihou & Xie, Hongmei & Hu, Taizhong, 2009. "Log-concavity of generalized order statistics," Statistics & Probability Letters, Elsevier, vol. 79(3), pages 396-399, February.
    11. Eric Beutner, 2008. "Nonparametric inference for sequential k-out-of-n systems," Annals of the Institute of Statistical Mathematics, Springer, vol. 60(3), pages 605-626, September.
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