Approximate maximum likelihood estimation for a spatial point pattern.

Mateu, Jorge, and Montes, Francisco. "Approximate maximum likelihood estimation for a spatial point pattern.." Qüestiió 24.1 (2000): 3-25. <http://eudml.org/doc/40299>.

@article{Mateu2000,
abstract = {Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have been proposed in the literature. We also present the results of a comparative simulation study developed for the Strauss model.},
author = {Mateu, Jorge, Montes, Francisco},
journal = {Qüestiió},
keywords = {Muestreo de Gibbs; Procesos estocásticos; Simulación de Montecarlo; Máxima verosimilitud; Gibbs distribution; Monte Carlo inference; stochastic approximation; Strauss model},
language = {eng},
number = {1},
pages = {3-25},
title = {Approximate maximum likelihood estimation for a spatial point pattern.},
url = {http://eudml.org/doc/40299},
volume = {24},
year = {2000},
}

TY - JOUR
AU - Mateu, Jorge
AU - Montes, Francisco
TI - Approximate maximum likelihood estimation for a spatial point pattern.
JO - Qüestiió
PY - 2000
VL - 24
IS - 1
SP - 3
EP - 25
AB - Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have been proposed in the literature. We also present the results of a comparative simulation study developed for the Strauss model.
LA - eng
KW - Muestreo de Gibbs; Procesos estocásticos; Simulación de Montecarlo; Máxima verosimilitud; Gibbs distribution; Monte Carlo inference; stochastic approximation; Strauss model
UR - http://eudml.org/doc/40299
ER -