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

  • 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.

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1007/s12076-008-0005-5
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Article provided by Springer in its journal Letters in Spatial and Resource Sciences.

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

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Handle: RePEc:spr:lsprsc:v:1:y:2008:i:1:p:45-54
Contact details of provider: Web page: http://www.springer.com/economics/journal/12076

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