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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Bee, M., Espa, G. A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data. Lett Spat Resour Sci 1, 45–54 (2008). https://doi.org/10.1007/s12076-008-0005-5
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DOI: https://doi.org/10.1007/s12076-008-0005-5