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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


Abstract

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
Date:2014
Institutions:Medicine > Lehrstuhl für Funktionelle Genomik
Physics > Institute of Theroretical Physics > Chair Professor Richter > Group Klaus Richter
Identification Number:
ValueType
10.1016/j.csda.2013.10.019DOI
Subjects:500 Science > 510 Mathematics
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Owner: Jack Kuipers
Deposited On:22 Jul 2011 07:19
Last Modified:28 Jan 2014 08:28
Item ID:21577
Owner Only: item control page
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