An MCMC computational approach for a continuous time state-dependent regime switching diffusion process
Artikel i vetenskaplig tidskrift, 2020

State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.

hidden states computation

random time imputation

Hybrid switching diffusion model

states computation

data imputation


El Houcine Hibbah

Sultan Mouly Slimane University

Hamid El Maroufy

Sultan Mouly Slimane University

Christiane Fuchs

German Research Center for Environmental Health

Technische Universität München

Universität Bielefeld

Ziad Taib

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

AstraZeneca AB

Journal of Applied Statistics

0266-4763 (ISSN) 1360-0532 (eISSN)

Vol. 47 8 1354-1374


Sannolikhetsteori och statistik



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