Skip to main content Skip to navigation

Event Diary

Show all calendar items

CRiSM Seminar - Randal Douc

- Export as iCalendar
Location: A1.01

Randal Douc (TELECOM SudParis)

Identifiability conditions for partially-observed Markov chains By R. Douc, F. Roueff and T. Sim

This paper deals with a parametrized family of partially-observed bivariate Markov chains. We establish that the limit of the normalized log-likelihood is maximized when the parameter belongs to the equivalence class of the true parameter, which is a key feature for obtaining consistency the Maximum Likelihood Estimators (MLE) in well-specified models. This result is obtained in a general framework including both fully dominated or partially dominated models, and thus applies to both Hidden Markov models or Observation-Driven times series. In contrast with previous approaches, the identifiability is addressed by relying on the unicity of the invariant distribution of the Markov chain associated to the complete data, regardless its rate of convergence to the equilibrium. We use this approach to obtain a set of easy-to-check conditions which imply the consistency of the MLE of a general observation driven time series.

Show all calendar items