IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i3p553-d1042182.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/3/553/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/3/553/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. Mameli, Valentina, 2015. "The Kumaraswamy skew-normal distribution," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 75-81.
    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Sher B. Chhetri & Alfred A. Akinsete & Gokarna Aryal & Hongwei Long, 2017. "The Kumaraswamy transmuted Pareto distribution," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-24, December.
    2. Anum Shafiq & Tabassum Naz Sindhu & Sanku Dey & Showkat Ahmad Lone & Tahani A. Abushal, 2023. "Statistical Features and Estimation Methods for Half-Logistic Unit-Gompertz Type-I Model," Mathematics, MDPI, vol. 11(4), pages 1-24, February.
    3. Devendra Kumar & Manoj Kumar, 2019. "A New Generalization of the Extended Exponential Distribution with an Application," Annals of Data Science, Springer, vol. 6(3), pages 441-462, September.
    4. Odom Conleth Chinazom & Nduka Ethelbert Chinaka & Ijomah Maxwell Azubuike, 2019. "The Marshall-Olkin Extended Weibull-Exponential Distribution: Properties and Applications," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 9(10), pages 158-172, October.
    5. 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.
    6. Narayanaswamy Balakrishnan & Laurent Bordes & Christian Paroissin & Jean-Christophe Turlot, 2016. "Single change-point detection methods for small lifetime samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 531-551, July.
    7. Mei Li & Wei Ning & Yubin Tian, 2024. "Change Point Test for Length-Biased Lognormal Distribution under Random Right Censoring," Mathematics, MDPI, vol. 12(11), pages 1-20, June.
    8. Tomasz J. Kozubowski & Krzysztof Podgórski, 2018. "Kumaraswamy Distribution and Random Extrema," The Open Statistics and Probability Journal, Bentham Open, vol. 9(1), pages 18-25, July.
    9. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    10. Gabriela Ciuperca & Zahraa Salloum, 2015. "Empirical likelihood test in a posteriori change-point nonlinear model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 919-952, November.
    11. Cai, Xia & Tian, Yubin & Ning, Wei, 2019. "Change-point analysis of the failure mechanisms based on accelerated life tests," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 515-522.
    12. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.
    13. 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.
    14. Mustafa Nadar & Fatih Kızılaslan, 2014. "Classical and Bayesian estimation of $$P(X>Y)$$ P ( X > Y ) using upper record values from Kumaraswamy’s distribution," Statistical Papers, Springer, vol. 55(3), pages 751-783, August.
    15. Mohamed Salah Eddine Arrouch & Echarif Elharfaoui & Joseph Ngatchou-Wandji, 2023. "Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models," Mathematics, MDPI, vol. 11(18), pages 1-31, September.
    16. Akram Kohansal, 2019. "On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample," Statistical Papers, Springer, vol. 60(6), pages 2185-2224, December.
    17. Vlad Stefan Barbu & Alex Karagrigoriou & Andreas Makrides, 2021. "Reliability and Inference for Multi State Systems: The Generalized Kumaraswamy Case," Mathematics, MDPI, vol. 9(16), pages 1-17, August.
    18. Shen, Gang, 2013. "On empirical likelihood inference of a change-point," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1662-1668.
    19. Kızılaslan, Fatih, 2017. "Classical and Bayesian estimation of reliability in a multicomponent stress–strength model based on the proportional reversed hazard rate mode," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 136(C), pages 36-62.
    20. Wei Ning, 2012. "Empirical likelihood ratio test for a mean change point model with a linear trend followed by an abrupt change," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 947-961, September.

    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:gam:jmathe:v:11:y:2023:i:3:p:553-:d:1042182. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.