MCMC imputation in autologistic model

2010 ◽  
Vol 16 (3-4) ◽  
Author(s):  
Marta Zalewska ◽  
Wojciech Niemiro ◽  
Bolesław Samoliński
Keyword(s):  
Autologistic Model

An Autologistic Model for the Spatial Distribution of Wildlife

1996 ◽  
Vol 33 (2) ◽  
pp. 339 ◽  
Author(s):  
N. H. Augustin ◽  
M. A. Mugglestone ◽  
S. T. Buckland
Keyword(s):  
Spatial Distribution ◽  
Autologistic Model

Variational Bayes and the Reduced Dependence Approximation for the Autologistic Model on an Irregular Grid With Applications

2012 ◽  
Vol 21 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Clare A. McGrory ◽  
Anthony N. Pettitt ◽  
Robert Reeves ◽  
Mark Griffin ◽  
Mark Dwyer
Keyword(s):  
Variational Bayes ◽  
Autologistic Model ◽  
Irregular Grid

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children

2011 ◽  
Vol 38 (12) ◽  
pp. 2763-2774 ◽  
Author(s):  
Solaiman Afroughi ◽  
Soghrat Faghihzadeh ◽  
Majid Jafari Khaledi ◽  
Mehdi Ghandehari Motlagh ◽  
Ebrahim Hajizadeh
Keyword(s):  
Dental Caries ◽  
Binary Data ◽  
Bayesian Method ◽  
Correlated Binary Data ◽  
Autologistic Model ◽  
Spatially Correlated

scholarly journals Location, Location, Location: An MCMC Approach to Modeling the Spatial Context of War and Peace

2002 ◽  
Vol 10 (3) ◽  
pp. 244-260 ◽  
Author(s):  
Michael D. Ward ◽  
Kristian Skrede Gleditsch
Keyword(s):  
Likelihood Function ◽  
War And Peace ◽  
Likelihood Methods ◽  
Autologistic Model ◽  
Unconditional Model ◽  
Out Of Sample ◽  
Maximum Likelihood Methods ◽  
Alternative Techniques ◽  
Categorical Response ◽  
Better Than

This article demonstrates how spatially dependent data with a categorical response variable can be addressed in a statistical model. We introduce the idea of an autologistic model where the response for one observation is dependent on the value of the response among adjacent observations. The autologistic model has likelihood function that is mathematically intractable, since the observations are conditionally dependent upon one another. We review alternative techniques for estimating this model, with special emphasis on recent advances using Markov chain Monte Carlo (MCMC) techniques. We evaluate a highly simplified autologistic model of conflict where the likelihood of war involvement for each nation is conditional on the war involvement of proximate states. We estimate this autologistic model for a single year (1988) via maximum pseudolikelihood and MCMC maximum likelihood methods. Our results indicate that the autologistic model fits the data much better than an unconditional model and that the MCMC estimates generally dominate the pseudolikelihood estimates. The autologistic model generates predicted probabilities greater than 0.5 and has relatively good predictive abilities in an out-of-sample forecast for the subsequent decade (1989 to 1998), correctly identifying not only ongoing conflicts, but also new ones.

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