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On asymptotic normality of pseudo likelihood estimates for pairwise interaction processes

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  • Jens Jensen
  • Hans Künsch

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  • Jens Jensen & Hans Künsch, 1994. "On asymptotic normality of pseudo likelihood estimates for pairwise interaction processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(3), pages 475-486, September.
    • Handle: RePEc:spr:aistmt:v:46:y:1994:i:3:p:475-486
    DOI: 10.1007/BF00773511
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Jean-François Coeurjolly & Ege Rubak, 2013. "Fast Covariance Estimation for Innovations Computed from a Spatial Gibbs Point Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 669-684, December.
    2. Levada Alexandre L., 2016. "Information geometry, simulation and complexity in Gaussian random fields," Monte Carlo Methods and Applications, De Gruyter, vol. 22(2), pages 81-107, June.
    3. Andrea Pallini, 2000. "Resampling configurations of points through coding schemes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 9(1), pages 159-182, January.
    4. Daniel, Jeffrey & Horrocks, Julie & Umphrey, Gary J., 2018. "Penalized composite likelihoods for inhomogeneous Gibbs point process models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 104-116.
    5. Winkler Gerhard, 2001. "A Stochastic Algorithm For Maximum Likelihood Estimation In Imaging," Statistics & Risk Modeling, De Gruyter, vol. 19(2), pages 101-120, February.
    6. repec:jss:jstsof:12:i06 is not listed on IDEAS
    7. Fuchun Huang & Yosihiko Ogata, 2002. "Generalized Pseudo-Likelihood Estimates for Markov Random Fields on Lattice," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(1), pages 1-18, March.
    8. Chen, Shyh-Huei & Ip, Edward H. & Wang, Yuchung J., 2011. "Gibbs ensembles for nearly compatible and incompatible conditional models," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1760-1769, April.
    9. Baddeley, Adrian & Turner, Rolf & Mateu, Jorge & Bevan, Andrew, 2013. "Hybrids of Gibbs Point Process Models and Their Implementation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i11).

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