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A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data

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  • Marco Bee

    ()

  • Giuseppe Espa

    ()

Abstract

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

Paper provided by Department of Economics, University of Trento, Italia in its series Department of Economics Working Papers with number 0801.

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Date of creation: 2008
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Handle: RePEc:trn:utwpde:0801

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Related research

Keywords: Spatial Missing Data; Monte Carlo EM Algorithm; Logistic Auto-logistic Model; Pseudo-Likelihood.;

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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, Geary Institute, University College Dublin 201215, Geary Institute, University College Dublin.

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