  Given a sequence of observations from a discrete-time, finite-state hidden
Markov model, we would like to estimate the sampling distribution of a
statistic. The bootstrap method is employed to approximate the confidence
regions of a multi-dimensional parameter. We propose an importance sampling
formula for efficient simulation in this context. Our approach consists of
constructing a locally asymptotically normal (LAN) family of probability
distributions around the default resampling rule and then minimizing the
asymptotic variance within the LAN family. The solution of this minimization
problem characterizes the asymptotically optimal resampling scheme, which is
given by a tilting formula. The implementation of the tilting formula is
facilitated by solving a Poisson equation. A few numerical examples are given
to demonstrate the efficiency of the proposed importance sampling scheme.
