IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i4d10.1007_s40745-021-00325-0.html
   My bibliography  Save this article

A Generalized-Alpha–Beta-Skew Normal Distribution with Applications

Author

Listed:
  • Sricharan Shah

    (Dibrugarh University)

  • Partha Jyoti Hazarika

    (Dibrugarh University)

  • Subrata Chakraborty

    (Dibrugarh University)

  • M. Masoom Ali

    (Ball State University)

Abstract

Recently there is a lot of research related to skewed distributions and their growing relevance in data analytics. In the present work we introduce a new generalized version of alpha beta skew normal distribution and some of its basic properties are investigated. Some extensions of the proposed distribution have also been studied. A simulation study has been conducted to see the performance of the obtained estimators of the parameters using Metropolis–Hastings (MH) algorithm. The appropriateness of the proposed distribution has been tested by comparing it with twelve closely related and nested distributions using Akaike Information Criterion. The Likelihood Ratio test has been employed for testing the relevance of the induction of the additional parameters in the proposed model.

Suggested Citation

  • Sricharan Shah & Partha Jyoti Hazarika & Subrata Chakraborty & M. Masoom Ali, 2023. "A Generalized-Alpha–Beta-Skew Normal Distribution with Applications," Annals of Data Science, Springer, vol. 10(4), pages 1127-1155, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00325-0
    DOI: 10.1007/s40745-021-00325-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-021-00325-0
    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/s40745-021-00325-0?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. Juárez, Miguel A. & Steel, Mark F. J., 2010. "Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-t Distributions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 52-66.
    2. Maryam Sharafi & Zahra Sajjadnia & Javad Behboodian, 2017. "A new generalization of alpha-skew-normal distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 6098-6111, June.
    3. V. Nekoukhou & M. Alamatsaz, 2012. "A family of skew-symmetric-Laplace distributions," Statistical Papers, Springer, vol. 53(3), pages 685-696, August.
    4. 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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Desmond Chekwube Bartholomew & Chrysogonus Chinagorom Nwaigwe & Ukamaka Cynthia Orumie & Godwin Onyeka Nwafor, 2024. "Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series," Annals of Data Science, Springer, vol. 11(5), pages 1609-1634, October.
    15. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
    16. Fabrizi, Enrico & Salvati, Nicola & Trivisano, Carlo, 2020. "Robust Bayesian small area estimation based on quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    17. Mahabuba Akhter & Syed Md. Minhaz Hossain & Rizma Sijana Nigar & Srabanti Paul & Khaleque Md. Aashiq Kamal & Anik Sen & Iqbal H. Sarker, 2024. "COVID-19 Fake News Detection using Deep Learning Model," Annals of Data Science, Springer, vol. 11(6), pages 2167-2198, December.
    18. Muhammad Tahir & Sufyan Ali & Ayesha Sohail & Ying Zhang & Xiaohua Jin, 2024. "Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects," Annals of Data Science, Springer, vol. 11(4), pages 1421-1434, August.
    19. Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024. "Modeling and Forecasting Macroeconomic Downside Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1010-1025, July.
    20. Junjie Hou & Yuqi Zhang & Duo Su, 2024. "Unified Image Harmonization with Region Augmented Attention Normalization," Annals of Data Science, Springer, vol. 11(5), pages 1865-1886, October.

    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:10:y:2023:i:4:d:10.1007_s40745-021-00325-0. 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.