A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data
AbstractThis 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 InfoPaper provided by Department of Economics, University of Trento, Italia in its series Department of Economics Working Papers with number 0801.
Date of creation: 2008
Date of revision:
Spatial Missing Data; Monte Carlo EM Algorithm; Logistic Auto-logistic Model; Pseudo-Likelihood.;
Other versions of this item:
- Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer, vol. 1(1), pages 45-54, April.
- Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data," Letters in Spatial and Resource Sciences, Springer, vol. 1(1), pages 45-54, July.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-03-08 (All new papers)
- NEP-ECM-2008-03-08 (Econometrics)
- NEP-ORE-2008-03-08 (Operations Research)
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- 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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