IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i6d10.1007_s40745-022-00382-z.html
   My bibliography  Save this article

Statistical Inference to the Parameter of the Akshaya Distribution under Competing Risks Data with Application HIV Infection to AIDS

Author

Listed:
  • Dina A. Ramadan

    (Mansoura University)

  • Ehab M. Almetwally

    (Delta University for Science and Technology
    Cairo University)

  • Ahlam H. Tolba

    (Mansoura University)

Abstract

This paper takes into consideration statistical inferences in competing risk models with Akshaya sub-distributions based on the type-II censoring scheme. It is supposed to be the k causes of failures. In the analysis of point and interval estimations of all model parameters, maximum likelihood and Bayesian procedures are applied. The Gibbs within Metropolis–Hasting samplers procedure is applied using the Markov chain Monte Carlo (MCMC) technique to get the Bayes estimates of the unknown parameters, their credible intervals (CRIs) and to estimate the relative risks. Furthermore, the survivor functions for subsystems and the overall system are evaluated. Finally, a real-life data set, which represents the times (in years) from HIV infection to AIDS and death in 329 men who had sex with men (MSM), is considered an application of the proposed methods.

Suggested Citation

  • Dina A. Ramadan & Ehab M. Almetwally & Ahlam H. Tolba, 2023. "Statistical Inference to the Parameter of the Akshaya Distribution under Competing Risks Data with Application HIV Infection to AIDS," Annals of Data Science, Springer, vol. 10(6), pages 1499-1525, December.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-022-00382-z
    DOI: 10.1007/s40745-022-00382-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00382-z
    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/s40745-022-00382-z?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. E. M. Almetwally & H. M. Almongy & M. K. Rastogi & M. Ibrahim, 2020. "Maximum Product Spacing Estimation of Weibull Distribution Under Adaptive Type-II Progressive Censoring Schemes," Annals of Data Science, Springer, vol. 7(2), pages 257-279, June.
    2. Ehab Mohamed Almetwally & Hiba Zeyada Muhammed & El-Sayed A. El-Sherpieny, 2020. "Bivariate Weibull Distribution: Properties and Different Methods of Estimation," Annals of Data Science, Springer, vol. 7(1), pages 163-193, March.
    3. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, 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. Hanan Haj Ahmad & Dina A. Ramadan & Ehab M. Almetwally, 2024. "Tampered Random Variable Analysis in Step-Stress Testing: Modeling, Inference, and Applications," Mathematics, MDPI, vol. 12(8), pages 1-25, April.

    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. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    2. 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.
    3. O. E. Abo-Kasem & Ehab M. Almetwally & Wael S. Abu El Azm, 2023. "Inferential Survival Analysis for Inverted NH Distribution Under Adaptive Progressive Hybrid Censoring with Application of Transformer Insulation," Annals of Data Science, Springer, vol. 10(5), pages 1237-1284, October.
    4. Mohamed Ibrahim & Khaoula Aidi & M. Masoom Ali & Haitham M. Yousof, 2023. "A Novel Test Statistic for Right Censored Validity under a new Chen extension with Applications in Reliability and Medicine," Annals of Data Science, Springer, vol. 10(5), pages 1285-1299, October.
    5. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    6. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    7. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    8. Amaal Elsayed Mubarak & Ehab Mohamed Almetwally, 2024. "Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models," Annals of Data Science, Springer, vol. 11(4), pages 1483-1502, August.
    9. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
    10. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    11. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    12. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    13. Sankalp Loomba & Madhavi Dave & Harshal Arolkar & Sachin Sharma, 2024. "Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency," Annals of Data Science, Springer, vol. 11(6), pages 2019-2034, December.
    14. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    15. Bo Li & Guangle Du, 2024. "Reaction Function for Financial Market Reacting to Events or Information," Annals of Data Science, Springer, vol. 11(4), pages 1265-1290, August.
    16. Tousifur Rahman & Partha Jyoti Hazarika & M. Masoom Ali & Manash Pratim Barman, 2022. "Three-Inflated Poisson Distribution and its Application in Suicide Cases of India During Covid-19 Pandemic," Annals of Data Science, Springer, vol. 9(5), pages 1103-1127, October.
    17. Vrushabh Gada & Madhura Shegaonkar & Madhura Inamdar & Sharath Dinesh & Darshan Sapariya & Vedant Konde & Mahesh Warang & Ninad Mehendale, 2022. "Data Analysis of COVID-19 Hospital Records Using Contextual Patient Classification System," Annals of Data Science, Springer, vol. 9(5), pages 945-965, October.
    18. Showkat Ahmad Lone & Intekhab Alam & Ahmadur Rahman, 2023. "Statistical Analysis Under Geometric Process in Accelerated Life Testing Plans for Generalized Exponential Distribution," Annals of Data Science, Springer, vol. 10(6), pages 1653-1665, December.
    19. Yanke Bao & Ying Wang, 2022. "Factor Space: The New Science of Causal Relationship," Annals of Data Science, Springer, vol. 9(3), pages 555-570, June.
    20. Manoj Verma & Harish Kumar Ghritlahre & Surendra Bajpai, 2023. "A Case Study of Optimization of a Solar Power Plant Sizing and Placement in Madhya Pradesh, India Using Multi-Objective Genetic Algorithm," Annals of Data Science, Springer, vol. 10(4), pages 933-966, August.

    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:10:y:2023:i:6:d:10.1007_s40745-022-00382-z. 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.