IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n4073799.html

Asymptotic Prediction for Future Observations of a Random Sample of Unknown Continuous Distribution

Author

Listed:
  • Magdy El-Adll
  • H. M. Barakat
  • Amany Aly

Abstract

When the first r lower extreme order statistics (failure times) of a sample of large size n, 1

Suggested Citation

  • Magdy El-Adll & H. M. Barakat & Amany Aly, 2022. "Asymptotic Prediction for Future Observations of a Random Sample of Unknown Continuous Distribution," Complexity, John Wiley & Sons, vol. 2022(1).
  • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:4073799
    DOI: 10.1155/2022/4073799
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/4073799
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/4073799?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
    ---><---

    References listed on IDEAS

    as
    1. Atef F. Hashem & Salem A. Alyami & Ahmed Mostafa Khalil, 2021. "Inference on a New Lifetime Distribution under Progressive Type II Censoring for a Parallel-Series Structure," Complexity, Hindawi, vol. 2021, pages 1-18, February.
    2. Said Alkarni & Ahmed Z. Afify & I. Elbatal & M. Elgarhy, 2020. "The Extended Inverse Weibull Distribution: Properties and Applications," Complexity, Hindawi, vol. 2020, pages 1-11, October.
    3. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    4. Magdy E. El-Adll, 2021. "Inference for the two-parameter exponential distribution with generalized order statistics," Mathematical Population Studies, Taylor & Francis Journals, vol. 28(1), pages 1-23, January.
    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. Magdy El-Adll & H. M. Barakat & Amany Aly & Ning Cai, 2022. "Asymptotic Prediction for Future Observations of a Random Sample of Unknown Continuous Distribution," Complexity, Hindawi, vol. 2022, pages 1-15, April.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús & Olmo, José, 2024. "Testing extreme warming and geographical heterogeneity," UC3M Working papers. Economics 45023, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2022. "Capital and Labor Income Pareto Exponents in the United States, 1916-2019," Papers 2206.04257, arXiv.org.
    4. González-Sánchez, Mariano & Nave Pineda, Juan M., 2023. "Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Frank Cowell & Emmanuel Flachaire, 2021. "Inequality Measurement: Methods and Data," Post-Print hal-03589066, HAL.
    6. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    7. Bernhard Klar, 2025. "A Pareto Tail Plot Without Moment Restrictions," The American Statistician, Taylor & Francis Journals, vol. 79(2), pages 156-166, April.
    8. Gareth W. Peters & Matteo Malavasi & Georgy Sofronov & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang, 2022. "Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity," Papers 2202.10588, arXiv.org, revised Mar 2023.
    9. Paweł D. Domański, 2024. "Energy-Aware Multicriteria Control Performance Assessment," Energies, MDPI, vol. 17(5), pages 1-18, March.
    10. Tjeerd de Vries & Alexis Akira Toda, 2022. "Capital and Labor Income Pareto Exponents Across Time and Space," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(4), pages 1058-1078, December.
    11. Gareth W. Peters & Matteo Malavasi & Georgy Sofronov & Pavel V. Shevchenko & Stefan Trück & Jiwook Jang, 2023. "Cyber loss model risk translates to premium mispricing and risk sensitivity," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(2), pages 372-433, April.
    12. Nagla Yahia & Najwan Alsadat, 2022. "[Retracted] A New Four‐Parameter Inverse Weibull Model: Statistical Properties and Applications," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    13. David Anthoff & Richard S. J. Tol, 2022. "Testing the Dismal Theorem," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 9(5), pages 885-920.
    14. Kan Chen & Tuoyuan Cheng, 2022. "Measuring Tail Risks," Papers 2209.07092, arXiv.org, revised Nov 2022.
    15. Ivanilda Cabral & Frederico Caeiro & M. Ivette Gomes, 2022. "On the comparison of several classical estimators of the extreme value index," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(1), pages 179-196, January.
    16. Laura Liu & Yulong Wang, 2025. "Binary Outcome Models with Extreme Covariates: Estimation and Prediction," Papers 2502.16041, arXiv.org.
    17. Emrah Altun & Hana N. Alqifari & Kadir Söyler, 2025. "Return Level Prediction with a New Mixture Extreme Value Model," Mathematics, MDPI, vol. 13(17), pages 1-24, August.
    18. Majdah Mohammed Badr & Ghaida Sobahi, 2022. "[Retracted] The Exponentiated Exponential‐Inverse Weibull Model: Theory and Application to COVID‐19 Data in Saudi Arabia," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    19. Man, Xinyue & Tang, Qihe, 2024. "Tail risk driven by investment losses and exogenous shocks," ASTIN Bulletin, Cambridge University Press, vol. 54(3), pages 712-737, September.
    20. Tuoyuan Cheng & Saikiran Reddy Poreddy & Kan Chen, 2025. "Tail Risk in Weather Derivatives," Commodities, MDPI, vol. 4(2), pages 1-17, June.

    More about this item

    Statistics

    Access and download statistics

    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:wly:complx:v:2022:y:2022:i:1:n:4073799. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.