Parameter estimation in pair hidden Markov models.
Elisabeth Gassiat, Ana Arribas-Gil and Catherine Matias
Scandinavian Journal of Statistics
This paper deals with parameter estimation in pair hidden Markov models
(pair-HMMs). We first
provide a rigorous formalism for these models and discuss possible
definitions of likelihoods. The model being biologically motivated, some
restrictions with respect to the full parameter space naturally occur.
Existence of two
different Information divergence rates is established and divergence
property (namely positivity at values different from the true one) is
shown under additional assumptions. This yields consistency for the parameter
in parametrization schemes for which the divergence property
holds. Simulations illustrate different cases which are not covered by our