IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v72y2010i1p89-109.html
   My bibliography  Save this article

Stochastic monotonicity of the MLEs of parameters in exponential simple step-stress models under Type-I and Type-II censoring

Author

Listed:
  • N. Balakrishnan
  • G. Iliopoulos

Abstract

No abstract is available for this item.

Suggested Citation

  • N. Balakrishnan & G. Iliopoulos, 2010. "Stochastic monotonicity of the MLEs of parameters in exponential simple step-stress models under Type-I and Type-II censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(1), pages 89-109, July.
    • Handle: RePEc:spr:metrik:v:72:y:2010:i:1:p:89-109
    DOI: 10.1007/s00184-009-0243-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00184-009-0243-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00184-009-0243-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. N. Balakrishnan & Qihao Xie & D. Kundu, 2009. "Exact inference for a simple step-stress model from the exponential distribution under time constraint," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 251-274, March.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maria Kateri & Udo Kamps, 2015. "Inference in step-stress models based on failure rates," Statistical Papers, Springer, vol. 56(3), pages 639-660, August.
    2. Benjamin Laumen & Erhard Cramer, 2021. "k‐step stage life testing," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 203-233, May.
    3. Julian Górny & Erhard Cramer, 2020. "On Exact Inferential Results for a Simple Step-Stress Model Under a Time Constraint," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 201-239, November.
    4. 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.
    5. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
    6. William Volterman & R. Arabi Belaghi & N. Balakrishnan, 2018. "Joint records from two exponential populations and associated inference," Computational Statistics, Springer, vol. 33(1), pages 549-562, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Julian Górny & Erhard Cramer, 2019. "From B-spline representations to gamma representations in hybrid censoring," Statistical Papers, Springer, vol. 60(4), pages 1119-1135, August.
    3. Julian Górny & Erhard Cramer, 2020. "On Exact Inferential Results for a Simple Step-Stress Model Under a Time Constraint," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 201-239, November.
    4. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
    5. Han, Donghoon & Balakrishnan, N., 2010. "Inference for a simple step-stress model with competing risks for failure from the exponential distribution under time constraint," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2066-2081, September.
    6. Tzong-Ru Tsai & Yuhlong Lio & Jyun-You Chiang & Yi-Jia Huang, 2022. "A New Process Performance Index for the Weibull Distribution with a Type-I Hybrid Censoring Scheme," Mathematics, MDPI, vol. 10(21), pages 1-17, November.
    7. Park, Sangun & Balakrishnan, N. & Zheng, Gang, 2008. "Fisher information in hybrid censored data," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2781-2786, November.
    8. Mohammad Vali Ahmadi & Jafar Ahmadi & Mousa Abdi, 2019. "Evaluating the lifetime performance index of products based on generalized order statistics from two-parameter exponential model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 251-275, April.
    9. Xiaojun Zhu & N. Balakrishnan & Helton Saulo, 2019. "On the existence and uniqueness of the maximum likelihood estimates of parameters of Laplace Birnbaum–Saunders distribution based on Type-I, Type-II and hybrid censored samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(7), pages 759-778, October.
    10. Tzong-Ru Tsai & Yuhlong Lio & Wei-Chen Ting, 2021. "EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    11. Teena Goyal & Piyush K. Rai & Sandeep K. Maurya, 2020. "Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 7(2), pages 307-345, June.
    12. Deepak Prajapati & Sharmistha Mitra & Debasis Kundu, 2019. "A New Decision Theoretic Sampling Plan for Type-I and Type-I Hybrid Censored Samples from the Exponential Distribution," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 251-288, December.
    13. Suparna Basu & Sanjay K. Singh & Umesh Singh, 2019. "Estimation of Inverse Lindley Distribution Using Product of Spacings Function for Hybrid Censored Data," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1377-1394, December.
    14. Jimut Bahan Chakrabarty & Shovan Chowdhury & Soumya Roy, 2019. "Optimum life test plan for products sold under warranty having Type-I generalizedhybrid censored Weibull distributed lifetimes," Working papers 302, Indian Institute of Management Kozhikode.
    15. B. Chandrasekar & A. Childs & N. Balakrishnan, 2004. "Exact likelihood inference for the exponential distribution under generalized Type‐I and Type‐II hybrid censoring," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(7), pages 994-1004, October.
    16. Balakrishnan, N. & Rasouli, Abbas, 2008. "Exact likelihood inference for two exponential populations under joint Type-II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2725-2738, January.
    17. Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.
    18. Feizjavadian, S.H. & Hashemi, R., 2015. "Analysis of dependent competing risks in the presence of progressive hybrid censoring using Marshall–Olkin bivariate Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 19-34.
    19. Julian Górny & Erhard Cramer, 2018. "Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(2), pages 173-210, February.
    20. N. Balakrishnan & Qihao Xie & D. Kundu, 2009. "Exact inference for a simple step-stress model from the exponential distribution under time constraint," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 251-274, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metrik:v:72:y:2010:i:1:p:89-109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.