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Dealing with monotone likelihood in a model for speckled data

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  • Pianto, Donald M.
  • Cribari-Neto, Francisco

Abstract

In this paper we study maximum likelihood estimation (MLE) of the roughness parameter of the distribution for speckled imagery (Frery et al., 1997). We discover that when a certain criterion is satisfied by the sample moments, the likelihood function is monotone and MLE estimates are infinite, implying an extremely homogeneous region. We implement three corrected estimators in an attempt to obtain finite parameter estimates. Two of the estimators are taken from the literature on monotone likelihood ([Firth, 1993] and [Jeffreys, 1946]) and one, based on resampling, is proposed by the authors. We perform Monte Carlo experiments to compare the three estimators. We find the estimator based on the Jeffreys prior to be the worst. The choice between Firth's estimator and the Bootstrap estimator depends on the value of the number of looks (which is given before estimation) and the specific needs of the user. We also apply the estimators to real data obtained from synthetic aperture radar (SAR). These results corroborate the Monte Carlo findings. Further clarification of the choice between the Firth and Bootstrap estimators will be obtained through future studies of the classification properties of these estimators.

Suggested Citation

  • Pianto, Donald M. & Cribari-Neto, Francisco, 2011. "Dealing with monotone likelihood in a model for speckled data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1394-1409, March.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:3:p:1394-1409
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    References listed on IDEAS

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    1. H. D. Pridmore, 1957. "Note," The Economic Record, The Economic Society of Australia, vol. 33(65), pages 265-267, August.
    2. Georg Heinze & Michael Schemper, 2001. "A Solution to the Problem of Monotone Likelihood in Cox Regression," Biometrics, The International Biometric Society, vol. 57(1), pages 114-119, March.
    3. Cribari-Neto, Francisco & Frery, Alejandro C. & Silva, Michel F., 2002. "Improved estimation of clutter properties in speckled imagery," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 801-824, October.
    4. Thaís C. O. Fonseca & Marco A. R. Ferreira & Helio S. Migon, 2008. "Objective Bayesian analysis for the Student-t regression model," Biometrika, Biometrika Trust, vol. 95(2), pages 325-333.
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    Cited by:

    1. Teuber, T. & Lang, A., 2012. "A new similarity measure for nonlocal filtering in the presence of multiplicative noise," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3821-3842.
    2. Girón, Edwin & Frery, Alejandro C. & Cribari-Neto, Francisco, 2012. "Nonparametric edge detection in speckled imagery," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(11), pages 2182-2198.
    3. Negreiros, Ana Cláudia Souza Vidal de & Lins, Isis Didier & Moura, Márcio José das Chagas & Droguett, Enrique López, 2020. "Reliability data analysis of systems in the wear-out phase using a (corrected) q-Exponential likelihood," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    4. Frederico Machado Almeida & Enrico Antônio Colosimo & Vinícius Diniz Mayrink, 2021. "Firth adjusted score function for monotone likelihood in the mixture cure fraction model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 131-155, January.
    5. Fonseca, Rodney V. & Cribari-Neto, Francisco, 2018. "Inference in a bimodal Birnbaum–Saunders model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 134-159.

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