IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i5p403-d228617.html
   My bibliography  Save this article

Skew-Reflected-Gompertz Information Quantifiers with Application to Sea Surface Temperature Records

Author

Listed:
  • Javier E. Contreras-Reyes

    (Departamento de Estadística, Facultad de Ciencias, Universidad del Bío-Bío, Concepción 4081112, Chile)

  • Mohsen Maleki

    (Department of Statistics, College of Sciences, Shiraz University, Shiraz 71946 85115, Iran)

  • Daniel Devia Cortés

    (Departamento de Evaluación de Pesquerías, Instituto de Fomento Pesquero, Valparaíso 2361827, Chile)

Abstract

The Skew-Reflected-Gompertz (SRG) distribution, introduced by Hosseinzadeh et al. (J. Comput. Appl. Math. (2019) 349, 132–141), produces two-piece asymmetric behavior of the Gompertz (GZ) distribution, which extends the positive to a whole dominion by an extra parameter. The SRG distribution also permits a better fit than its well-known classical competitors, namely the skew-normal and epsilon-skew-normal distributions, for data with a high presence of skewness. In this paper, we study information quantifiers such as Shannon and Rényi entropies, and Kullback–Leibler divergence in terms of exact expressions of GZ information measures. We find the asymptotic test useful to compare two SRG-distributed samples. Finally, as a real-world data example, we apply these results to South Pacific sea surface temperature records.

Suggested Citation

  • Javier E. Contreras-Reyes & Mohsen Maleki & Daniel Devia Cortés, 2019. "Skew-Reflected-Gompertz Information Quantifiers with Application to Sea Surface Temperature Records," Mathematics, MDPI, vol. 7(5), pages 1-14, May.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:403-:d:228617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/5/403/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/5/403/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohsen Maleki & Mohammad Reza Mahmoudi, 2017. "Two-Piece location-scale distributions based on scale mixtures of normal family," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(24), pages 12356-12369, December.
    2. Bondon, Pascal, 2009. "Estimation of autoregressive models with epsilon-skew-normal innovations," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1761-1776, September.
    3. Contreras-Reyes, Javier E., 2015. "Rényi entropy and complexity measure for skew-gaussian distributions and related families," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 84-91.
    4. Adam Lenart & Trifon I. Missov, 2016. "Goodness-of-fit tests for the Gompertz distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(10), pages 2920-2937, May.
    5. Salicru, M. & Morales, D. & Menendez, M. L. & Pardo, L., 1994. "On the Applications of Divergence Type Measures in Testing Statistical Hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 372-391, November.
    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. W. V. Félix de Lima & A. D. C. Nascimento & G. J. A. Amaral, 2021. "Entropy-based pivotal statistics for multi-sample problems in planar shape," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 153-178, March.
    2. Wegenkittl, Stefan, 2002. "A Generalized [phi]-Divergence for Asymptotically Multivariate Normal Models," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 288-302, November.
    3. Martín, N. & Balakrishnan, N., 2013. "Hypothesis testing in a generic nesting framework for general distributions," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 1-23.
    4. Abraão Nascimento & Jodavid Ferreira & Alisson Silva, 2023. "Divergence-based tests for the bivariate gamma distribution applied to polarimetric synthetic aperture radar," Statistical Papers, Springer, vol. 64(5), pages 1439-1463, October.
    5. Alba-Fernández, V. & Jiménez-Gamero, M.D., 2009. "Bootstrapping divergence statistics for testing homogeneity in multinomial populations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(12), pages 3375-3384.
    6. Atefeh Zarei & Zahra Khodadadi & Mohsen Maleki & Karim Zare, 2023. "Robust mixture regression modeling based on two-piece scale mixtures of normal distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 181-210, March.
    7. Zheng, Tingguo & Xiao, Han & Chen, Rong, 2015. "Generalized ARMA models with martingale difference errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 492-506.
    8. Conde, J. & Salicrú, M., 1998. "Uniform association in contingency tables associated to Csiszar divergence," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 149-154, February.
    9. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Contreras-Reyes, Javier E., 2022. "Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    11. Tomáš Hobza & Domingo Morales & Leandro Pardo, 2014. "Divergence-based tests of homogeneity for spatial data," Statistical Papers, Springer, vol. 55(4), pages 1059-1077, November.
    12. Mohsen Maleki & Darren Wraith & Reinaldo B. Arellano-Valle, 2019. "A flexible class of parametric distributions for Bayesian linear mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 543-564, June.
    13. Clécio da Silva Ferreira & Gilberto A. Paula & Gustavo C. Lana, 2022. "Estimation and diagnostic for partially linear models with first-order autoregressive skew-normal errors," Computational Statistics, Springer, vol. 37(1), pages 445-468, March.
    14. Valdevino Félix de Lima, Wenia & David Costa do Nascimento, Abraão & José Amorim do Amaral, Getúlio, 2021. "Distance-based tests for planar shape," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    15. M. Sharafi & A. R. Nematollahi, 2016. "AR(1) model with skew-normal innovations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 1011-1029, November.
    16. Contreras-Reyes, Javier E., 2021. "Lerch distribution based on maximum nonsymmetric entropy principle: Application to Conway’s game of life cellular automaton," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    17. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    18. Mora, Ricardo & Ruiz-Castillo, Javier, 2010. "A Kullback-Leibler measure of conditional segregation," UC3M Working papers. Economics we1015, Universidad Carlos III de Madrid. Departamento de Economía.
    19. Alejandro C. Frery & Juliana Gambini, 2020. "Comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 359-378, June.
    20. Menéndez, M. L. & Morales, D. & Pardo, L. & Zografos, K., 1999. "Statistical inference for finite Markov chains based on divergences," Statistics & Probability Letters, Elsevier, vol. 41(1), pages 9-17, January.

    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:7:y:2019:i:5:p:403-:d:228617. 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.