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A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data


Author Info

  • Marco Bee


  • Giuseppe Espa



This paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.

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Bibliographic Info

Article provided by Springer in its journal Letters in Spatial and Resource Sciences.

Volume (Year): 1 (2008)
Issue (Month): 1 (April)
Pages: 45-54

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Handle: RePEc:spr:astaws:v:1:y:2008:i:1:p:45-54

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Related research

Keywords: Spatial missing data; Monte Carlo EM algorithm; Logistic auto-logistic model; Pseudo-likelihood; C13; C15; C51;

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Cited by:
  1. Raffaella Calabrese & Johan A. Elkink, 2012. "Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study," Working Papers 201215, Geary Institute, University College Dublin.


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