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Exact likelihood inference for the two-parameter exponential distribution under Type-II progressively hybrid censoring

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  • Ping Chan
  • Hon Ng
  • Feng Su

Abstract

Hybrid censoring schemes are commonly used in life-testing experiments to reduce the experimental time and the cost. A Type-II progressive hybrid censoring scheme (PHCS) was introduced by Kundu and Joarder (Comput Stat Data Anal 50:2509–2528, 2006 ) that combines progressive Type-II censoring and Type-I censoring. In this paper, we consider the statistical inference of a two-parameter exponential distribution under the Type-II PHCS. The conditional maximum likelihood estimates (MLEs) of the model parameters and their joint and marginal conditional moment generating functions are derived. Based on these exact conditional moments, bias-reduced estimators are proposed and their distributions are discussed. Confidence intervals of the model parameters based on exact and asymptotic distributions of the MLEs and bias-reduced estimators are developed. The performances of the point and interval estimation procedures are evaluated and compared through exact calculations and Monte Carlo simulations. Recommendations are made based on these results and an illustrative example is presented. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Ping Chan & Hon Ng & Feng Su, 2015. "Exact likelihood inference for the two-parameter exponential distribution under Type-II progressively hybrid censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(6), pages 747-770, August.
    • Handle: RePEc:spr:metrik:v:78:y:2015:i:6:p:747-770
    DOI: 10.1007/s00184-014-0525-5
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    References listed on IDEAS

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    1. Kundu, Debasis & Joarder, Avijit, 2006. "Analysis of Type-II progressively hybrid censored data," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2509-2528, June.
    2. N. Balakrishnan, 2007. "Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 211-259, August.
    3. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
    4. Balakrishnan, N. & Childs, A. & Chandrasekar, B., 2002. "An efficient computational method for moments of order statistics under progressive censoring," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 359-365, December.
    5. N. Balakrishnan, 2007. "Rejoinder on: Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 290-296, August.
    6. A. Childs & B. Chandrasekar & N. Balakrishnan & D. Kundu, 2003. "Exact likelihood inference based on Type-I and Type-II hybrid censored samples from the exponential distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 319-330, June.
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    Cited by:

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    2. J. Ahmadi & B. Khatib Astaneh & M. Rezaie & S. Ameli, 2022. "Prediction of times to failure of censored units under generalized progressive hybrid censoring scheme," Computational Statistics, Springer, vol. 37(4), pages 2049-2086, September.
    3. Arnab Koley & Debasis Kundu, 2017. "On generalized progressive hybrid censoring in presence of competing risks," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(4), pages 401-426, May.
    4. El-Sherpieny, El-Sayed A. & Almetwally, Ehab M. & Muhammed, Hiba Z., 2020. "Progressive Type-II hybrid censored schemes based on maximum product spacing with application to Power Lomax distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    5. Kyeongjun Lee & Youngseuk Cho, 2017. "Bayesian and maximum likelihood estimations of the inverted exponentiated half logistic distribution under progressive Type II censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 811-832, April.
    6. van Bentum, Thomas & Cramer, Erhard, 2019. "Stochastic monotonicity of MLEs of the mean for exponentially distributed lifetimes under hybrid censoring," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 1-8.
    7. Ritwik Bhattacharya & Biswabrata Pradhan, 2017. "Computation of optimum Type-II progressively hybrid censoring schemes using variable neighborhood search algorithm," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 802-821, December.
    8. Kyeongjun Lee, 2024. "Inference for Parameters of Exponential Distribution under Combined Type II Progressive Hybrid Censoring Scheme," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    9. Rui Hua & Wenhao Gui, 2022. "Inference for copula-based dependent competing risks model with step-stress accelerated life test under generalized progressive hybrid censoring," Computational Statistics, Springer, vol. 37(5), pages 2399-2436, November.
    10. Zhang, Fode & Shi, Yimin & Wang, Ruibing, 2017. "Geometry of the q-exponential distribution with dependent competing risks and accelerated life testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 552-565.
    11. J. M. Lennartz & S. Bedbur & U. Kamps, 2021. "Minimum area confidence regions and their coverage probabilities for type-II censored exponential data," Statistical Papers, Springer, vol. 62(1), pages 171-191, February.
    12. M. M. Mohie El-Din & M. Nagy & M. H. Abu-Moussa, 2019. "Estimation and Prediction for Gompertz Distribution Under the Generalized Progressive Hybrid Censored Data," Annals of Data Science, Springer, vol. 6(4), pages 673-705, December.

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