IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v12y2025i2d10.1007_s40745-024-00516-5.html
   My bibliography  Save this article

A New Hyperbolic Tangent Family of Distributions: Properties and Applications

Author

Listed:
  • Shahid Mohammad

    (University of Wisconsin Oshkosh)

  • Isabel Mendoza

    (University of Wisconsin Oshkosh)

Abstract

This paper introduces a new family of distributions called the hyperbolic tangent (HT) family. The cumulative distribution function of this model is defined using the standard hyperbolic tangent function. The fundamental properties of the distribution are thoroughly examined and presented. Additionally, an inverse exponential distribution is employed as a sub-model within the HT family, and its properties are also derived. The parameters of the HT family are estimated using the maximum likelihood method, and the performance of these estimators is assessed using a simulation approach. To demonstrate the significance and flexibility of the newly introduced family of distributions, two real data sets are utilized. These data sets serve as practical examples that showcase the applicability and usefulness of the HT family in real-world scenarios. By introducing the HT family, exploring its properties, employing the maximum likelihood estimation, and conducting simulations and real data analyses, this paper contributes to the advancement of statistical modeling and distribution theory.

Suggested Citation

  • Shahid Mohammad & Isabel Mendoza, 2025. "A New Hyperbolic Tangent Family of Distributions: Properties and Applications," Annals of Data Science, Springer, vol. 12(2), pages 457-480, April.
  • Handle: RePEc:spr:aodasc:v:12:y:2025:i:2:d:10.1007_s40745-024-00516-5
    DOI: 10.1007/s40745-024-00516-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-024-00516-5
    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/s40745-024-00516-5?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. Hadeel Klakattawi & Dawlah Alsulami & Mervat Abd Elaal & Sanku Dey & Lamya Baharith, 2022. "A new generalized family of distributions based on combining Marshal-Olkin transformation with T-X family," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-29, February.
    2. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    3. El-Sayed A. El-Sherpieny & Mamhoud M. Elsehetry, 2019. "Type II Kumaraswamy Half Logistic Family of Distributions with Applications to Exponential Model," Annals of Data Science, Springer, vol. 6(1), pages 1-20, March.
    4. Ibrahim Alkhairy & M. Nagy & Abdisalam Hassan Muse & Eslam Hussam & Sameh S. Askar, 2021. "The Arctan-X Family of Distributions: Properties, Simulation, and Applications to Actuarial Sciences," Complexity, Hindawi, vol. 2021, pages 1-14, December.
    5. Zubair Ahmad, 2020. "A New Generalized Class of Distributions: Properties and Estimation Based on Type-I Censored Samples," Annals of Data Science, Springer, vol. 7(2), pages 243-256, June.
    6. Zubair Ahmad, 2020. "The Zubair-G Family of Distributions: Properties and Applications," Annals of Data Science, Springer, vol. 7(2), pages 195-208, June.
    7. 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.
    8. Suleman Nasiru, 2018. "Extended Odd Fréchet-G Family of Distributions," Journal of Probability and Statistics, Hindawi, vol. 2018, pages 1-12, December.
    9. Lea Anzagra & Solomon Sarpong & Suleman Nasiru, 2022. "Odd Chen-G Family of Distributions," Annals of Data Science, Springer, vol. 9(2), pages 369-391, April.
    10. Mustapha Muhammad & Lixia Liu & Badamasi Abba & Isyaku Muhammad & Mouna Bouchane & Hexin Zhang & Sani Musa, 2023. "A New Extension of the Topp–Leone-Family of Models with Applications to Real Data," Annals of Data Science, Springer, vol. 10(1), pages 225-250, February.
    11. Zubair Ahmad, 2019. "The Hyperbolic Sine Rayleigh Distribution with Application to Bladder Cancer Susceptibility," Annals of Data Science, Springer, vol. 6(2), pages 211-222, June.
    12. Abdulkareem M. Basheer, 2022. "Marshall–Olkin Alpha Power Inverse Exponential Distribution: Properties and Applications," Annals of Data Science, Springer, vol. 9(2), pages 301-313, April.
    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. Sandeep Kumar Maurya & Saralees Nadarajah, 2021. "Poisson Generated Family of Distributions: A Review," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 484-540, November.
    2. Kousik Maiti & Suchandan Kayal & Aditi Kar Gangopadhyay, 2024. "On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data," Annals of Data Science, Springer, vol. 11(5), pages 1761-1798, October.
    3. Tapan Kumar Chakrabarty & Dreamlee Sharma, 2021. "A Generalization of the Quantile-Based Flattened Logistic Distribution," Annals of Data Science, Springer, vol. 8(3), pages 603-627, September.
    4. Lea Anzagra & Solomon Sarpong & Suleman Nasiru, 2022. "Odd Chen-G Family of Distributions," Annals of Data Science, Springer, vol. 9(2), pages 369-391, April.
    5. Gayan Warahena-Liyanage & Broderick Oluyede & Thatayaone Moakofi & Whatmore Sengweni, 2023. "The New Exponentiated Half Logistic-Harris-G Family of Distributions with Actuarial Measures and Applications," Stats, MDPI, vol. 6(3), pages 1-29, July.
    6. Adebisi A. Ogunde & Subhankar Dutta & Ehab M. Almetawally, 2025. "Half Logistic Generalized Rayleigh Distribution for Modeling Hydrological Data," Annals of Data Science, Springer, vol. 12(2), pages 667-694, April.
    7. Broderick Oluyede & Thatayaone Moakofi, 2022. "Type II Exponentiated Half-Logistic-Gompertz Topp-Leone-G Family of Distributions with Applications," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 14(4), pages 225-262, December.
    8. Harshita Kumawat & Kanak Modi & Pankaj Nagar, 2024. "Modi-Weibull Distribution: Inferential and Simulation Study," Annals of Data Science, Springer, vol. 11(6), pages 1975-1999, December.
    9. Boikanyo Makubate & Fastel Chipepa & Broderick Oluyede & Peter O. Peter, 2021. "The Marshall-Olkin Half Logistic-G Family of Distributions With Applications," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 120-120, March.
    10. Mahmoud Aldeni & Carl Lee & Felix Famoye, 2017. "Families of distributions arising from the quantile of generalized lambda distribution," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-18, December.
    11. 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.
    12. Naif Alotaibi & A. S. Al-Moisheer & Ibrahim Elbatal & Mansour Shrahili & Mohammed Elgarhy & Ehab M. Almetwally, 2023. "Half Logistic Inverted Nadarajah–Haghighi Distribution under Ranked Set Sampling with Applications," Mathematics, MDPI, vol. 11(7), pages 1-32, April.
    13. 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.
    14. Ramadan A. ZeinEldin & Christophe Chesneau & Farrukh Jamal & Mohammed Elgarhy, 2019. "Statistical Properties and Different Methods of Estimation for Type I Half Logistic Inverted Kumaraswamy Distribution," Mathematics, MDPI, vol. 7(10), pages 1-24, October.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. A. A. Ogunde & S. T. Fayose & B. Ajayi & D. O. Omosigho, 2020. "Properties, Inference and Applications of Alpha Power Extended Inverted Weibull Distribution," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(6), pages 1-90, November.
    20. 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.

    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:12:y:2025:i:2:d:10.1007_s40745-024-00516-5. 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.