IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p942-d771557.html
   My bibliography  Save this article

Extended Half-Power Exponential Distribution with Applications to COVID-19 Data

Author

Listed:
  • Karol I. Santoro

    (Departamento de Matemática, Facultad de Ciencias, Universidad Católica del Norte, Antofagasta 1240000, Chile
    These authors contributed equally to this work.)

  • Héctor J. Gómez

    (Departamento de Ciencias Matemáticas y Físicas, Facultad de Ingeniería, Universidad Católica de Temuco, Temuco 4780000, Chile
    These authors contributed equally to this work.)

  • Inmaculada Barranco-Chamorro

    (Departamento de Estadística e Investigación Operativa, Universidad de Sevilla, 41012 Sevilla, Spain
    These authors contributed equally to this work.)

  • Héctor W. Gómez

    (Departamento de Matemática, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile
    These authors contributed equally to this work.)

Abstract

In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed.

Suggested Citation

  • Karol I. Santoro & Héctor J. Gómez & Inmaculada Barranco-Chamorro & Héctor W. Gómez, 2022. "Extended Half-Power Exponential Distribution with Applications to COVID-19 Data," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:942-:d:771557
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/942/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/6/942/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Choy, S. T. Boris & Walker, Stephen G., 2003. "The extended exponential power distribution and Bayesian robustness," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 227-232, November.
    2. Saralees Nadarajah, 2005. "A generalized normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 685-694.
    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. Nanami Taketomi & Kazuki Yamamoto & Christophe Chesneau & Takeshi Emura, 2022. "Parametric Distributions for Survival and Reliability Analyses, a Review and Historical Sketch," Mathematics, MDPI, vol. 10(20), pages 1-23, October.

    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. Tumlinson, Samuel E., 2015. "On the non-existence of maximum likelihood estimates for the extended exponential power distribution and its generalizations," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 111-114.
    2. García, V.J. & Gómez-Déniz, E. & Vázquez-Polo, F.J., 2010. "A new skew generalization of the normal distribution: Properties and applications," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2021-2034, August.
    3. Müller K. & Richter W.-D., 2016. "Exact distributions of order statistics of dependent random variables from ln,p-symmetric sample distributions, n ∈ {3,4}," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-29, February.
    4. Sandi Baressi Šegota & Nikola Anđelić & Mario Šercer & Hrvoje Meštrić, 2022. "Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data," Mathematics, MDPI, vol. 10(7), pages 1-17, April.
    5. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    6. Kapla, Daniel & Fertl, Lukas & Bura, Efstathia, 2022. "Fusing sufficient dimension reduction with neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    7. Saralees Nadarajah, 2006. "Acknowledgement of Priority: the Generalized Normal Distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 1031-1032.
    8. Lassance, Nathan & Vrins, Frédéric, 2023. "Portfolio selection: A target-distribution approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 302-314.
    9. Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    10. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
    11. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," IAST Working Papers 20-106, Institute for Advanced Study in Toulouse (IAST).
    12. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.
    13. Li, Liuling & Mizrach, Bruce, 2010. "Tail return analysis of Bear Stearns' credit default swaps," Economic Modelling, Elsevier, vol. 27(6), pages 1529-1536, November.
    14. Fung, Thomas & Seneta, Eugene, 2008. "A characterisation of scale mixtures of the uniform distribution," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2883-2888, December.
    15. Mijeong Kim & Yanyuan Ma, 2019. "Semiparametric efficient estimators in heteroscedastic error models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(1), pages 1-28, February.
    16. Jorge Munoz-Minjares & Osbaldo Vite-Chavez & Jorge Flores-Troncoso & Jorge M. Cruz-Duarte, 2021. "Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians," Mathematics, MDPI, vol. 9(18), pages 1-19, September.
    17. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    18. Roger W. Barnard & Kent Pearce & A. Alexandre Trindade, 2018. "When is tail mean estimation more efficient than tail median? Answers and implications for quantitative risk management," Annals of Operations Research, Springer, vol. 262(1), pages 47-65, March.
    19. Punzo, Antonio & Bagnato, Luca, 2021. "Modeling the cryptocurrency return distribution via Laplace scale mixtures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    20. Zhou, Tong & Peng, Yongbo, 2020. "Adaptive Bayesian quadrature based statistical moments estimation for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).

    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:10:y:2022:i:6:p:942-:d:771557. 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.