IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v29y2020i2d10.1007_s11749-019-00658-2.html
   My bibliography  Save this article

Comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance

Author

Listed:
  • Alejandro C. Frery

    (Universidade Federal de Alagoas)

  • Juliana Gambini

    (Instituto Tecnológico de Buenos Aires
    Universidad Nacional de Tres de Febrero)

Abstract

The $${\mathcal {G}}^0$$G0 distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the $${\mathcal {G}}^0$$G0 distribution using a geodesic distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the $${\mathcal {G}}^0$$G0 distribution. We propose three tests based on the GD which combine the tests presented in Naranjo-Torres et al. (IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):987–997, 2017), and we estimate their probability distributions using permutation methods.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:2:d:10.1007_s11749-019-00658-2
    DOI: 10.1007/s11749-019-00658-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11749-019-00658-2
    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/s11749-019-00658-2?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. 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.
    2. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    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. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    2. Mazen Nassar & Refah Alotaibi & Ahmed Elshahhat, 2023. "Reliability Estimation of XLindley Constant-Stress Partially Accelerated Life Tests using Progressively Censored Samples," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    3. Wegenkittl, Stefan, 2002. "A Generalized [phi]-Divergence for Asymptotically Multivariate Normal Models," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 288-302, November.
    4. Sanjeev Bakshi & Shailendra Kumar Mishra, 2024. "On measures of elder abuse: investigating the intensity and extent in seven states of India," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 26(2), pages 396-408, August.
    5. John-Fritz Thony & Jean Vaillant, 2022. "Parameter Estimation for a Fractional Black–Scholes Model with Jumps from Discrete Time Observations," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    6. Badamasi Abba & Hong Wang, 2024. "A new failure times model for one and two failure modes system: A Bayesian study with Hamiltonian Monte Carlo simulation," Journal of Risk and Reliability, , vol. 238(2), pages 304-323, April.
    7. 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.
    8. Logar, Ivana & Brouwer, Roy & Campbell, Danny, 2020. "Does attribute order influence attribute-information processing in discrete choice experiments?," Resource and Energy Economics, Elsevier, vol. 60(C).
    9. Padayachee Trishanta & Khamiakova Tatsiana & Shkedy Ziv & Salo Perttu & Perola Markus & Burzykowski Tomasz, 2019. "A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-13, April.
    10. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    11. Refah Alotaibi & Mazen Nassar & Hoda Rezk & Ahmed Elshahhat, 2022. "Inferences and Engineering Applications of Alpha Power Weibull Distribution Using Progressive Type-II Censoring," Mathematics, MDPI, vol. 10(16), pages 1-21, August.
    12. 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.
    13. Muhammet Burak Kılıç & Yusuf Şahin & Melih Burak Koca, 2021. "Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation," Computational Statistics, Springer, vol. 36(2), pages 1219-1242, June.
    14. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
    15. 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.
    16. Teresa Backhaus, 2022. "Training in Late Careers - A Structural Approach," CRC TR 224 Discussion Paper Series crctr224_2022_382, University of Bonn and University of Mannheim, Germany.
    17. 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.
    18. 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.
    19. Hancock, Thomas O. & Broekaert, Jan & Hess, Stephane & Choudhury, Charisma F., 2020. "Quantum choice models: A flexible new approach for understanding moral decision-making," Journal of choice modelling, Elsevier, vol. 37(C).
    20. Granado-Díaz, Rubén & Villanueva, Anastasio J. & Gómez-Limón, José A., 2022. "Willingness to accept for rewilding farmland in environmentally sensitive areas," Land Use Policy, Elsevier, vol. 116(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:spr:testjl:v:29:y:2020:i:2:d:10.1007_s11749-019-00658-2. 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.