IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v22y2020i3d10.1007_s11009-019-09765-x.html
   My bibliography  Save this article

Purely Sequential and k-Stage Procedures for Estimating the Mean of an Inverse Gaussian Distribution

Author

Listed:
  • Ajit Chaturvedi

    (University of Delhi)

  • Sudeep R. Bapat

    (University of California)

  • Neeraj Joshi

    (University of Delhi)

Abstract

In the first part of this paper, we propose purely sequential and k-stage (k ≥ 3) procedures for estimation of the mean μ of an inverse Gaussian distribution having prescribed ‘proportional closeness’. The problem is constructed in such a manner that the boundedness of the expected loss is equivalent to the estimation of parameter with given ‘proportional closeness’. We obtain the associated second-order approximations for both the procedures. Second part of this paper deals with developing the minimum risk and bounded risk point estimation problems for estimating the mean μ of an inverse Gaussian distribution having unknown scale parameter λ. We propose an useful family of loss functions for both the problems and our aim is to control the associated risk functions. Moreover, we establish the failure of fixed sample size procedures to deal with these problems and hence propose purely sequential and k-stage (k ≥ 3) procedures to estimate the mean μ. We also obtain the second-order approximations associated with our sequential procedures. Further, we provide extensive sets of simulation studies and real data analysis to show the performances of our proposed procedures.

Suggested Citation

  • Ajit Chaturvedi & Sudeep R. Bapat & Neeraj Joshi, 2020. "Purely Sequential and k-Stage Procedures for Estimating the Mean of an Inverse Gaussian Distribution," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 1193-1219, September.
  • Handle: RePEc:spr:metcap:v:22:y:2020:i:3:d:10.1007_s11009-019-09765-x
    DOI: 10.1007/s11009-019-09765-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-019-09765-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11009-019-09765-x?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. Sudeep R. Bapat, 2018. "On purely sequential estimation of an inverse Gaussian mean," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 1005-1024, November.
    2. N. Mukhopadhyay & T. Solanky, 1997. "Estimation after sequential selection and ranking," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 45(1), pages 95-106, January.
    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. Ajit Chaturvedi & Sudeep R. Bapat & Neeraj Joshi, 2022. "Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 402-420, May.

    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. Sudeep R. Bapat, 2023. "A novel sequential approach to estimate functions of parameters of two gamma populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(6), pages 627-641, August.
    2. Nitis Mukhopadhyay & Tumulesh K. S. Solanky, 2012. "On Two-Stage Comparisons with a Control Under Heteroscedastic Normal Distributions," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 501-522, September.
    3. Bandyopadhyay, Uttam & Sarkar, Suman & Biswas, Atanu, 2022. "Sequential confidence interval for comparing two Bernoulli distributions in a non-conventional set-up," Statistics & Probability Letters, Elsevier, vol. 181(C).
    4. Kazuyoshi Yata & Makoto Aoshima, 2012. "Inference on High-Dimensional Mean Vectors with Fewer Observations Than the Dimension," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 459-476, September.
    5. Ajit Chaturvedi & Sudeep R. Bapat & Neeraj Joshi, 2022. "Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 402-420, May.
    6. Nitis Mukhopadhyay & Srawan Kumar Bishnoi, 2021. "An Unusual Application of Cramér-Rao Inequality to Prove the Attainable Lower Bound for a Ratio of Complicated Gamma Functions," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1507-1517, December.
    7. Nitis Mukhopadhyay & Soumik Banerjee, 2023. "A General Theory of Three-Stage Estimation Strategy with Second-Order Asymptotics and Its Applications," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 401-440, February.
    8. Hwang, Leng-Cheng, 2020. "A robust two-stage procedure for the Poisson process under the linear exponential loss function," Statistics & Probability Letters, Elsevier, vol. 163(C).
    9. Christopher S. Withers & Saralees Nadarajah, 2022. "Cornish-Fisher Expansions for Functionals of the Weighted Partial Sum Empirical Distribution," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1791-1804, September.
    10. Bhargab Chattopadhyay & Nitis Mukhopadhyay, 2019. "Constructions of New Classes of One- and Two-Sample Nonparametric Location Tests," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1229-1249, December.
    11. Nitis Mukhopadhyay, 2010. "On a Sharper Lower Bound for a Percentile of a Student’s t Distribution with an Application," Methodology and Computing in Applied Probability, Springer, vol. 12(4), pages 609-622, December.
    12. Makoto Aoshima & Kazuyoshi Yata, 2015. "Asymptotic Normality for Inference on Multisample, High-Dimensional Mean Vectors Under Mild Conditions," Methodology and Computing in Applied Probability, Springer, vol. 17(2), pages 419-439, June.
    13. S. Zacks, 2007. "Review of Some Functionals of Compound Poisson Processes and Related Stopping Times," Methodology and Computing in Applied Probability, Springer, vol. 9(2), pages 343-356, June.

    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:metcap:v:22:y:2020:i:3:d:10.1007_s11009-019-09765-x. 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.