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Sequential Monte Carlo EM for multivariate probit models

Moffa, Giusi and Kuipers, Jack (2014) Sequential Monte Carlo EM for multivariate probit models. Comp. Stats. & Data An. 72, pp. 252-272.

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Other URL: http://arxiv.org/abs/1107.2205, http://dx.doi.org/10.1016/j.csda.2013.10.019


Multivariate probit models (MPM) have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for the dependence modelling is the covariance matrix of an underlying latent multivariate Gaussian. Most approaches to MLE in multivariate probit regression rely on MCEM algorithms to avoid computationally intensive ...


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Item type:Article
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Physics > Institute of Theroretical Physics > Chair Professor Richter > Group Klaus Richter
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Dewey Decimal Classification:500 Science > 510 Mathematics
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Item ID:21577
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