85 research outputs found
Non singularity of the asymptotic Fisher information matrix in hidden Markov models
In this paper, we consider a parametric hidden Markov model where the hidden
state space is non necessarily finite. We provide a necessary and sufficient
condition for the invertibility of the limiting Fisher information matrix
A vanilla Rao--Blackwellization of Metropolis--Hastings algorithms
Casella and Robert [Biometrika 83 (1996) 81--94] presented a general
Rao--Blackwellization principle for accept-reject and Metropolis--Hastings
schemes that leads to significant decreases in the variance of the resulting
estimators, but at a high cost in computation and storage. Adopting a
completely different perspective, we introduce instead a universal scheme that
guarantees variance reductions in all Metropolis--Hastings-based estimators
while keeping the computation cost under control. We establish a central limit
theorem for the improved estimators and illustrate their performances on toy
examples and on a probit model estimation.Comment: Published in at http://dx.doi.org/10.1214/10-AOS838 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Handy sufficient conditions for the convergence of the maximum likelihood estimator in observation-driven models
This paper generalizes asymptotic properties obtained in the
observation-driven times series models considered by \cite{dou:kou:mou:2013} in
the sense that the conditional law of each observation is also permitted to
depend on the parameter. The existence of ergodic solutions and the consistency
of the Maximum Likelihood Estimator (MLE) are derived under easy-to-check
conditions. The obtained conditions appear to apply for a wide class of models.
We illustrate our results with specific observation-driven times series,
including the recently introduced NBIN-GARCH and NM-GARCH models, demonstrating
the consistency of the MLE for these two models
Comparison of Resampling Schemes for Particle Filtering
This contribution is devoted to the comparison of various resampling
approaches that have been proposed in the literature on particle filtering. It
is first shown using simple arguments that the so-called residual and
stratified methods do yield an improvement over the basic multinomial
resampling approach. A simple counter-example showing that this property does
not hold true for systematic resampling is given. Finally, some results on the
large-sample behavior of the simple bootstrap filter algorithm are given. In
particular, a central limit theorem is established for the case where
resampling is performed using the residual approach
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