We consider a hidden Markov model (HMM) with multidimensional observations, and where
the coefficients (transition probability matrix, and observation conditional densities) depend
on some unknown parameter. We study the asymptotic behaviour of two recursive
estimators, the recursive maximum likelihood estimator (RMLE), and the recursive
conditional least squares estimator (RCLSE), as the number of observations increases to
infinity. Firstly, we exhibit the contrast functions associated with the two non-recursive …

We consider a hidden Markov model (HMM) with multidimensional observations, and where
the coe cients (transition probability matrix, and observation conditional densities) depend
on some unknown parameter. We study the asymptotic behaviour of two recursive
estimators, the recursive maximum likelihood estimator (RMLE), and the recursive
conditional least squares estimator (RCLSE), as the number of observations increases to in
nity. Firstly, we exhibit the contrast functions associated with the two non {recursive …