IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v9y2022i2d10.1007_s40745-020-00309-6.html
   My bibliography  Save this article

Predictive Inference and Parameter Estimation from the Half-Normal Distribution for the Left Censored Data

Author

Listed:
  • Tabassum Naz Sindhu

    (Quaid-i-Azam University 45320
    The National University of Computer and Emerging Sciences)

  • Zawar Hussain

    (Cholistan University of Veterinary and Animal Sciences)

Abstract

In present article, we consider predictive inference and the Bayesian estimation for the parameter assuming that the given left censored data follow the half-normal distribution. Predictive density function for a single future response, a bivariate future response, and several future responses is obtained by incorporating the posterior density function. To derive the posterior and predictive distribution result on the basis of the left censored data, a Bayesian framework was employed in conjunction with an informative prior. Also, the Bayesian structure was utilized in association with informative and uninformative priors to obtain the Bayes estimators on the basis of different loss functions. Simulated left censored samples from a half-normal distribution are utilized to interpret the results. Posterior risks of the Bayes estimators are evaluated and compared to explore the effect of prior belief and loss functions. Bayes estimators are efficient under quasi quadratic loss function using the square root gamma prior.

Suggested Citation

  • Tabassum Naz Sindhu & Zawar Hussain, 2022. "Predictive Inference and Parameter Estimation from the Half-Normal Distribution for the Left Censored Data," Annals of Data Science, Springer, vol. 9(2), pages 285-299, April.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:2:d:10.1007_s40745-020-00309-6
    DOI: 10.1007/s40745-020-00309-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00309-6
    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/s40745-020-00309-6?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. M. S. Eliwa & M. El-Morshedy, 2019. "Bivariate Gumbel-G Family of Distributions: Statistical Properties, Bayesian and Non-Bayesian Estimation with Application," Annals of Data Science, Springer, vol. 6(1), pages 39-60, March.
    2. Manoj Kumar & Anurag Pathak & Sukriti Soni, 2019. "Bayesian Inference for Rayleigh Distribution Under Step-Stress Partially Accelerated Test with Progressive Type-II Censoring with Binomial Removal," Annals of Data Science, Springer, vol. 6(1), pages 117-152, March.
    Full references (including those not matched with items on IDEAS)

    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. Varun Agiwal, 2023. "Bayesian Estimation of Stress Strength Reliability from Inverse Chen Distribution with Application on Failure Time Data," Annals of Data Science, Springer, vol. 10(2), pages 317-347, April.
    2. Devendra Kumar & M. Nassar & Sanku Dey, 2023. "Progressive Type-II Censored Data and Associated Inference with Application Based on Li–Li Rayleigh Distribution," Annals of Data Science, Springer, vol. 10(1), pages 43-71, February.
    3. Mohamed Ibrahim & M. Masoom Ali & Haitham M. Yousof, 2023. "The Discrete Analogue of the Weibull G Family: Properties, Different Applications, Bayesian and Non-Bayesian Estimation Methods," Annals of Data Science, Springer, vol. 10(4), pages 1069-1106, August.
    4. M. El-Morshedy & Ziyad Ali Alhussain & Doaa Atta & Ehab M. Almetwally & M. S. Eliwa, 2020. "Bivariate Burr X Generator of Distributions: Properties and Estimation Methods with Applications to Complete and Type-II Censored Samples," Mathematics, MDPI, vol. 8(2), pages 1-31, February.
    5. Aliyu Ismail Ishaq & Alfred Adewole Abiodun, 2020. "The Maxwell–Weibull Distribution in Modeling Lifetime Datasets," Annals of Data Science, Springer, vol. 7(4), pages 639-662, December.
    6. Manuel Franco & Juana-María Vivo & Debasis Kundu, 2020. "A Generator of Bivariate Distributions: Properties, Estimation, and Applications," Mathematics, MDPI, vol. 8(10), pages 1-30, October.
    7. Paula Ianishi & Oilson Alberto Gonzatto Junior & Marcos Jardel Henriques & Diego Carvalho do Nascimento & Gabriel Kamada Mattar & Pedro Luiz Ramos & Anderson Ara & Francisco Louzada, 2022. "Probability on Graphical Structure: A Knowledge-Based Agricultural Case," Annals of Data Science, Springer, vol. 9(2), pages 327-345, April.
    8. Muhammad H. Tahir & Muhammad Adnan Hussain & Gauss M. Cordeiro & M. El-Morshedy & M. S. Eliwa, 2020. "A New Kumaraswamy Generalized Family of Distributions with Properties, Applications, and Bivariate Extension," Mathematics, MDPI, vol. 8(11), pages 1-28, November.
    9. M. S. Eliwa & Ziyad Ali Alhussain & M. El-Morshedy, 2020. "Discrete Gompertz-G Family of Distributions for Over- and Under-Dispersed Data with Properties, Estimation, and Applications," Mathematics, MDPI, vol. 8(3), pages 1-26, March.
    10. Anurag Pathak & Manoj Kumar & Sanjay Kumar Singh & Umesh Singh & Manoj Kumar Tiwari & Sandeep Kumar, 2022. "Bayesian inference for Maxwell Boltzmann distribution on step-stress partially accelerated life test under progressive type-II censoring with binomial removals," 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. 13(4), pages 1976-2010, August.
    11. Hiba Z. Muhammed & Ehab M. Almetwally, 2023. "Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type-II Censoring," Annals of Data Science, Springer, vol. 10(2), pages 481-512, April.
    12. Abhimanyu Singh Yadav & Subhradev Sen & Sudhansu S. Maiti & Mahendra Saha & Shivanshi Shukla, 2023. "Some Further Properties and Bayesian Inference for Inverse xgamma Distribution Under Progressive Type-II Censored Scheme," Annals of Data Science, Springer, vol. 10(2), pages 455-479, April.
    13. Harshita Kumawat & Kanak Modi & Pankaj Nagar, 2024. "Modi-Weibull Distribution: Inferential and Simulation Study," Annals of Data Science, Springer, vol. 11(6), pages 1975-1999, December.
    14. Mohamed A. W. Mahmoud & Mohamed G. M. Ghazal & Hossam M. M. Radwan, 2023. "Bayesian Estimation and Optimal Censoring of Inverted Generalized Linear Exponential Distribution Using Progressive First Failure Censoring," Annals of Data Science, Springer, vol. 10(2), pages 527-554, April.

    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:aodasc:v:9:y:2022:i:2:d:10.1007_s40745-020-00309-6. 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.