A self-consistency approach to multinomial logit model with random effects |
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Authors: | Shufang Wang Alex Tsodikov |
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Institution: | Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA |
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Abstract: | The computation in the multinomial logit mixed effects model is costly especially when the response variable has a large number of categories, since it involves high-dimensional integration and maximization. Tsodikov and Chefo (2008) developed a stable MLE approach to problems with independent observations, based on generalized self-consistency and quasi-EM algorithm developed in Tsodikov (2003). In this paper, we apply the idea to clustered multinomial response to simplify the maximization step. The method transforms the complex multinomial likelihood to Poisson-type likelihood and hence allows for the estimates to be obtained iteratively solving a set of independent low-dimensional problems. The methodology is applied to real data and studied by simulations. While maximization is simplified, numerical integration remains the dominant challenge to computational efficiency. |
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Keywords: | QEM algorithm Multinomial logit model with random effects |
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