IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i3p1394-1409.html
   My bibliography  Save this article

Dealing with monotone likelihood in a model for speckled data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00372-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    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.

    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. 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).
    2. Chau, Nancy H. & Qin, Yu & Zhang, Weiwen, 2015. "Networked Leaders in the Shadow of the Market – A Chinese Experiment in Allocating Land Conversion Rights," Working Papers 250022, Cornell University, Department of Applied Economics and Management.
    3. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    4. Antonio Parisi & B. Liseo, 2018. "Objective Bayesian analysis for the multivariate skew-t model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 277-295, June.
    5. Lior Rennert & Zichen Ma & Christopher S. McMahan & Delphine Dean, 2022. "Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    6. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    7. 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.
    8. Aparecida Souza & Helio Migon, 2010. "Bayesian outlier analysis in binary regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1355-1368.
    9. 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.
    10. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    11. Aldo M. Garay & Heleno Bolfarine & Victor H. Lachos & Celso R.B. Cabral, 2015. "Bayesian analysis of censored linear regression models with scale mixtures of normal distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2694-2714, December.
    12. Debdeep Pati & David Dunson, 2014. "Bayesian nonparametric regression with varying residual density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 1-31, February.
    13. Everett, Bethany G. & Wall, Melanie & Shea, Eileen & Hughes, Tonda L., 2021. "Mortality risk among a sample of sexual minority women: A focus on the role of sexual identity disclosure," Social Science & Medicine, Elsevier, vol. 272(C).
    14. Maalouf, Maher & Trafalis, Theodore B., 2011. "Robust weighted kernel logistic regression in imbalanced and rare events data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 168-183, January.
    15. Wang, Min & Yang, Mingan, 2016. "Posterior property of Student-t linear regression model using objective priors," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 23-29.
    16. Ricardo S. Ehlers, 2011. "Comparison of Bayesian models for production efficiency," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2433-2443, January.
    17. Eun Ju Cho & Jeong-Hoon Lee & Yuri Cho & Yun Bin Lee & Jeong-Ju Yoo & Minjong Lee & Dong Hyeon Lee & Su Jong Yu & Yoon Jun Kim & Jung-Hwan Yoon & Hyo-Suk Lee, 2015. "Comparison of the Efficacy of Entecavir and Tenofovir in Nucleos(T)ide Analogue-Experienced Chronic Hepatitis B Patients," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-10, June.
    18. Wei Wang & Shou‐En Lu & Jerry Q. Cheng & Minge Xie & John B. Kostis, 2022. "Multivariate survival analysis in big data: A divide‐and‐combine approach," Biometrics, The International Biometric Society, vol. 78(3), pages 852-866, September.
    19. Villa, Cristiano & Rubio, Francisco J., 2018. "Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 197-219.
    20. Cristiano C. Santos & Rosangela H. Loschi, 2017. "Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 209-230, March.

    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:eee:csdana:v:55:y:2011:i:3:p:1394-1409. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.