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Change Point Analysis for Kumaraswamy Distribution

Author

Listed:
  • Weizhong Tian

    (College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China)

  • Liyuan Pang

    (School of Science, Xi’an University of Technology, Xi’an 710048, China)

  • Chengliang Tian

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Wei Ning

    (Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA)

Abstract

The Kumaraswamy distribution is a common type of bounded distribution, which is widely used in agriculture, hydrology, and other fields. In this paper, we use the methods of the likelihood ratio test, modified information criterion, and Schwarz information criterion to analyze the change point of the Kumaraswamy distribution. Simulation experiments give the performance of the three methods. The application section illustrates the feasibility of the proposed method by applying it to a real dataset.

Suggested Citation

  • Weizhong Tian & Liyuan Pang & Chengliang Tian & Wei Ning, 2023. "Change Point Analysis for Kumaraswamy Distribution," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:553-:d:1042182
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    References listed on IDEAS

    as
    1. Robert King & Irene Lena Hudson & Muhammad Shuaib Khan, 2016. "Transmuted Kumaraswamy Distribution," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 183-210, June.
    2. Mustafa Nadar & Alexander Papadopoulos & Fatih Kızılaslan, 2013. "Statistical analysis for Kumaraswamy’s distribution based on record data," Statistical Papers, Springer, vol. 54(2), pages 355-369, May.
    3. Mameli, Valentina, 2015. "The Kumaraswamy skew-normal distribution," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 75-81.
    4. Zou, Changliang & Liu, Yukun & Qin, Peng & Wang, Zhaojun, 2007. "Empirical likelihood ratio test for the change-point problem," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 374-382, February.
    5. Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.
    6. Rashad A. R. Bantan & Christophe Chesneau & Farrukh Jamal & Mohammed Elgarhy & Waleed Almutiry & Amani Abdullah Alahmadi, 2021. "Study of a Modified Kumaraswamy Distribution," Mathematics, MDPI, vol. 9(21), pages 1-26, November.
    7. Xia Cai & Khamis Khalid Said & Wei Ning, 2016. "Change-point analysis with bathtub shape for the exponential distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2740-2750, November.
    8. Muhammad Shuaib Khan & Robert King & Irene Lena Hudson, 2016. "Transmuted Kumaraswamy Distribution," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 183-210, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Jorge Figueroa-Zúñiga & Juan G. Toledo & Bernardo Lagos-Alvarez & Víctor Leiva & Jean P. Navarrete, 2023. "Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile," Mathematics, MDPI, vol. 11(13), pages 1-14, June.

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