Parametric inference for mixed models defined  by stochastic differential equations

Sophie Donnet; Adeline Samson

Displaying similar documents to “Parametric inference for mixed models defined by stochastic differential equations”

Coupling a stochastic approximation version of EM with an MCMC procedure

Estelle Kuhn, Marc Lavielle (2004)

ESAIM: Probability and Statistics

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The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with markovian perturbations are used...

Stochastic algorithm for Bayesian mixture effect template estimation

Stéphanie Allassonnière, Estelle Kuhn (2010)

ESAIM: Probability and Statistics

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The estimation of probabilistic deformable template models in computer vision or of probabilistic atlases in Computational Anatomy are core issues in both fields. A first coherent statistical framework where the geometrical variability is modelled as a hidden random variable has been given by [S. Allassonnière , (2007) 3–29]. They introduce a Bayesian approach and mixture of them to estimate deformable template models. A consistent stochastic algorithm has been introduced...

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

Grigorova, Denitsa, Encheva, Elitsa, Gueorguieva, Ralitza (2013)

Serdica Journal of Computing

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The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in...