IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v74y2011i2p167-183.html

Maximum likelihood and restricted maximum likelihood estimation for a class of Gaussian Markov random fields

Author

Listed:
  • Victor De Oliveira

  • Marco Ferreira

Abstract

No abstract is available for this item.

Suggested Citation

  • Victor De Oliveira & Marco Ferreira, 2011. "Maximum likelihood and restricted maximum likelihood estimation for a class of Gaussian Markov random fields," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(2), pages 167-183, September.
    • Handle: RePEc:spr:metrik:v:74:y:2011:i:2:p:167-183
    DOI: 10.1007/s00184-009-0295-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00184-009-0295-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00184-009-0295-7?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Ferreira, Marco A.R. & De Oliveira, Victor, 2007. "Bayesian reference analysis for Gaussian Markov random fields," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 789-812, April.
    3. Håvard Rue, 2001. "Fast sampling of Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 325-338.
    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. Kęstutis Dučinskas & Lina Dreižienė, 2018. "Risks of Classification of the Gaussian Markov Random Field Observations," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 422-436, October.
    2. Lisa Henn & James S. Hodges, 2014. "Multiple Local Maxima in Restricted Likelihoods and Posterior Distributions for Mixed Linear Models," International Statistical Review, International Statistical Institute, vol. 82(1), pages 90-105, April.
    3. Li, Mengyuan & Yu, Dalei & Bai, Peng, 2013. "A note on the existence and uniqueness of quasi-maximum likelihood estimators for mixed regressive, spatial autoregression models," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 568-572.
    4. 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).

    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. Vinicius Mayrink & Dani Gamerman, 2009. "On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms," Computational Statistics, Springer, vol. 24(4), pages 641-669, December.
    2. Ying C. MacNab, 2018. "Rejoinder on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 554-569, September.
    3. Volker Schmid & Leonhard Held, 2004. "Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data," Biometrics, The International Biometric Society, vol. 60(4), pages 1034-1042, December.
    4. Ferreira, Marco A.R. & De Oliveira, Victor, 2007. "Bayesian reference analysis for Gaussian Markov random fields," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 789-812, April.
    5. Samson B. Adebayo, 2004. "Bayesian geoadditive modelling of breastfeeding initiation in Nigeria," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 267-281.
    6. 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).
    7. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    8. Riccardo Borgoni & Francesco C. Billari, 2002. "Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse," MPIDR Working Papers WP-2002-048, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Riccardo Borgoni & Francesco Billari, 2003. "Bayesian spatial analysis of demographic survey data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 8(3), pages 61-92.
    10. Gamerman, Dani & Moreira, Ajax R. B. & Rue, Havard, 2003. "Space-varying regression models: specifications and simulation," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 513-533, March.
    11. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    12. Stefan Lang & Samson B. Adebayo & Ludwig Fahrmeir & Winfried J. Steiner, 2003. "Bayesian Geoadditive Seemingly Unrelated Regression," Computational Statistics, Springer, vol. 18(2), pages 263-292, July.
    13. Leonhard Knorr-Held & Günter Raßer & Nikolaus Becker, 2002. "Disease Mapping of Stage-Specific Cancer Incidence Data," Biometrics, The International Biometric Society, vol. 58(3), pages 492-501, September.
    14. Miklos Arato, N. & Dryden, Ian L. & Taylor, Charles C., 2006. "Hierarchical Bayesian modelling of spatial age-dependent mortality," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1347-1363, November.
    15. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    16. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    17. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.
    18. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    19. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    20. Thomas C. McHale & Claudia M. Romero-Vivas & Claudio Fronterre & Pedro Arango-Padilla & Naomi R. Waterlow & Chad D. Nix & Andrew K. Falconar & Jorge Cano, 2019. "Spatiotemporal Heterogeneity in the Distribution of Chikungunya and Zika Virus Case Incidences during their 2014 to 2016 Epidemics in Barranquilla, Colombia," IJERPH, MDPI, vol. 16(10), pages 1-21, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:metrik:v:74:y:2011:i:2:p:167-183. 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.