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Some binary start-up demonstration tests and associated inferential methods

Author

Listed:
  • N. Balakrishnan
  • M. Koutras
  • F. Milienos

Abstract

During the past few decades, substantial research has been carried out on start-up demonstration tests. In this paper, we study the class of binary start-up demonstration tests under a general framework. Assuming that the outcomes of the start-up tests are described by a sequence of exchangeable random variables, we develop a general form for the exact waiting time distribution associated with the length of the test (i.e., number of start-ups required to decide on the acceptance or rejection of the equipment/unit under inspection). Approximations for the tail probabilities of this distribution are also proposed. Moreover, assuming that the probability of a successful start-up follows a beta distribution, we discuss several estimation methods for the parameters of the beta distribution, when several types of observed data have been collected from a series of start-up tests. Finally, the performance of these estimation methods and the accuracy of the suggested approximations for the tail probabilities are illustrated through numerical experimentation. Copyright The Institute of Statistical Mathematics, Tokyo 2014

Suggested Citation

  • N. Balakrishnan & M. Koutras & F. Milienos, 2014. "Some binary start-up demonstration tests and associated inferential methods," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 759-787, August.
  • Handle: RePEc:spr:aistmt:v:66:y:2014:i:4:p:759-787
    DOI: 10.1007/s10463-013-0424-y
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    References listed on IDEAS

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    1. DePoy Smith, Michelle L. & Griffith, William S., 2008. "The analysis and comparison of start-up demonstration tests," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1029-1045, May.
    2. Martin, Donald E. K., 2004. "Markovian start-up demonstration tests with rejection of units upon observing d failures," European Journal of Operational Research, Elsevier, vol. 155(2), pages 474-486, June.
    3. N. Balakrishnan & P. Chan, 2000. "Start-Up Demonstration Tests with Rejection of Units upon Observing d Failures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(1), pages 184-196, March.
    4. N. Balakrishnan & S. Mohanty & S. Aki, 1997. "Start-Up Demonstration Tests Under Markov Dependence Model with Corrective Actions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(1), pages 155-169, March.
    5. Martin, Donald E.K., 2008. "Application of auxiliary Markov chains to start-up demonstration tests," European Journal of Operational Research, Elsevier, vol. 184(2), pages 574-583, January.
    6. P. S. Chan & H. K. T. Ng & N. Balakrishnan, 2008. "Statistical inference for start-up demonstration tests with rejection of units upon observing d failures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(8), pages 867-878.
    7. Ram Tripathi & Ramesh Gupta & John Gurland, 1994. "Estimation of parameters in the beta binomial model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 317-331, June.
    8. David Scollnik, 2010. "Bayesian statistical inference for start-up demonstration tests with rejection of units upon observing d failures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1113-1121.
    9. Lee, Jack C & Sabavala, Darius J, 1987. "Bayesian Estimation and Prediction for the Beta-Binomial Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 357-367, July.
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    Cited by:

    1. N. Balakrishnan & M. V. Koutras & F. S. Milienos, 2017. "On the identifiability of start-up demonstration mixture models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 717-735, August.
    2. Kiyoshi Inoue, 2021. "On Homogeneous Multivariate Distributions in Random Occupancy Models and Their Applications," Methodology and Computing in Applied Probability, Springer, vol. 23(3), pages 1129-1153, September.

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