IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v11y2024i2d10.1007_s40745-022-00453-1.html
   My bibliography  Save this article

Statistical Analysis from the Generalized Inverse Lindley Distribution with Adaptive Type-II Progressively Hybrid Censoring Scheme

Author

Listed:
  • Intekhab Alam

    (St. Andrews Institute of Technology & Management)

  • Murshid Kamal

    (Aligarh Muslim University)

  • Mohammad Tariq Intezar

    (GD Goenka University)

  • Saqib Showkat Wani

    (Guru Nanak Dev Engineering College)

  • Imran Alam

    (Al-Barkat College of Graduate Studies)

Abstract

The key assumption in accelerated life testing is that the mathematical model concerning the lifetime of the item and the stress is known or can be assumed. In several situations, such life-stress relationships are not known and cannot be assumed, i.e. accelerated life testing information cannot be extrapolated to use situation. So, in such cases, a partially accelerated life test is a more appropriate testing method to be executed for which tested objects are subjected to both normal and accelerated circumstances. Due to continual improvement in manufacturing design, it is more difficult to obtain information about the lifetime of products or materials with high reliability at the time of testing under normal conditions. An approach to accelerate failures is the step-stress partially accelerated life test which increases the load applied to the goods in a particular discrete sequence. In this study, the maximum likelihood estimators of inverse the generalized inverse Lindley distribution parameters and the acceleration factor are investigated in a step-stress partially accelerated life test model utilizing two various types of progressively hybrid censoring systems. Furthermore, the performance of the model parameter estimators with the two progressive hybrid censoring schemes is analyzed and compared in terms of biases and mean squared errors using a Monte Carlo simulation approach.

Suggested Citation

  • Intekhab Alam & Murshid Kamal & Mohammad Tariq Intezar & Saqib Showkat Wani & Imran Alam, 2024. "Statistical Analysis from the Generalized Inverse Lindley Distribution with Adaptive Type-II Progressively Hybrid Censoring Scheme," Annals of Data Science, Springer, vol. 11(2), pages 479-506, April.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:2:d:10.1007_s40745-022-00453-1
    DOI: 10.1007/s40745-022-00453-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00453-1
    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/s40745-022-00453-1?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. Ling, Li & Xu, Wei & Li, Minghai, 2009. "Parametric inference for progressive Type-I hybrid censored data on a simple step-stress accelerated life test model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3110-3121.
    2. Vikas Kumar Sharma & Sanjay Kumar Singh & Umesh Singh & Faton Merovci, 2016. "The generalized inverse Lindley distribution: A new inverse statistical model for the study of upside-down bathtub data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(19), pages 5709-5729, October.
    3. Amal S. Hassan & Said G. Nassr, 2019. "Power Lindley-G Family of Distributions," Annals of Data Science, Springer, vol. 6(2), pages 189-210, June.
    4. Morris H. Degroot & Prem K. Goel, 1979. "Bayesian estimation and optimal designs in partially accelerated life testing," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 26(2), pages 223-235, June.
    5. 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)

    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. Ismail, Ali A., 2019. "Statistical analysis of Type-I progressively hybrid censored data under constant-stress life testing model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 138-150.
    2. Intekhab Alam & Mustafa Kamal & Ahmadur Rahman & Trapty Agarwal & Awakash Mishra, 2024. "Statistical analysis using multiple censoring scheme under partially accelerated life tests for the power Lindley distribution," 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. 15(7), pages 3424-3436, July.
    3. Friday Ikechukwu Agu & Joseph Thomas Eghwerido, 2021. "Agu-Eghwerido distribution, regression model and applications," Statistics in Transition New Series, Polish Statistical Association, vol. 22(4), pages 59-76, December.
    4. 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.
    5. 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.
    6. 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.
    7. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    8. Satti R. G. Reddy & G. P. Saradhi Varma & Rajya Lakshmi Davuluri, 2024. "Deep Neural Network (DNN) Mechanism for Identification of Diseased and Healthy Plant Leaf Images Using Computer Vision," Annals of Data Science, Springer, vol. 11(1), pages 243-272, February.
    9. Astha Modi & Khelan Shah & Shrey Shah & Samir Patel & Manan Shah, 2024. "Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis," Annals of Data Science, Springer, vol. 11(1), pages 159-180, February.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Broderick Oluyede & Thatayaone Moakofi, 2023. "The Gamma-Topp-Leone-Type II-Exponentiated Half Logistic-G Family of Distributions with Applications," Stats, MDPI, vol. 6(2), pages 1-28, June.
    15. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    16. Elham Shamsinejad & Touraj Banirostam & Mir Mohsen Pedram & Amir Masoud Rahmani, 2025. "A Review of Anonymization Algorithms and Methods in Big Data," Annals of Data Science, Springer, vol. 12(1), pages 253-279, February.
    17. A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    18. 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.
    19. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    20. 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.

    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:11:y:2024:i:2:d:10.1007_s40745-022-00453-1. 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.