17,209 research outputs found
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Maximum likelihood estimators are often of limited practical use due to the
intensive computation they require. We propose a family of alternative
estimators that maximize a stochastic variation of the composite likelihood
function. Each of the estimators resolve the computation-accuracy tradeoff
differently, and taken together they span a continuous spectrum of
computation-accuracy tradeoff resolutions. We prove the consistency of the
estimators, provide formulas for their asymptotic variance, statistical
robustness, and computational complexity. We discuss experimental results in
the context of Boltzmann machines and conditional random fields. The
theoretical and experimental studies demonstrate the effectiveness of the
estimators when the computational resources are insufficient. They also
demonstrate that in some cases reduced computational complexity is associated
with robustness thereby increasing statistical accuracy.Comment: 30 pages, 97 figures, 2 author
Optimality of estimators for misspecified semi-Markov models
Suppose we observe a geometrically ergodic semi-Markov process and have a
parametric model for the transition distribution of the embedded Markov chain,
for the conditional distribution of the inter-arrival times, or for both. The
first two models for the process are semiparametric, and the parameters can be
estimated by conditional maximum likelihood estimators. The third model for the
process is parametric, and the parameter can be estimated by an unconditional
maximum likelihood estimator. We determine heuristically the asymptotic
distributions of these estimators and show that they are asymptotically
efficient. If the parametric models are not correct, the (conditional) maximum
likelihood estimators estimate the parameter that maximizes the
Kullback--Leibler information. We show that they remain asymptotically
efficient in a nonparametric sense.Comment: To appear in a Special Volume of Stochastics: An International
Journal of Probability and Stochastic Processes
(http://www.informaworld.com/openurl?genre=journal%26issn=1744-2508) edited
by N.H. Bingham and I.V. Evstigneev which will be reprinted as Volume 57 of
the IMS Lecture Notes Monograph Series
(http://imstat.org/publications/lecnotes.htm
Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes
Composite likelihoods are increasingly used in applications where the full
likelihood is analytically unknown or computationally prohibitive. Although the
maximum composite likelihood estimator has frequentist properties akin to those
of the usual maximum likelihood estimator, Bayesian inference based on
composite likelihoods has yet to be explored. In this paper we investigate the
use of the Metropolis--Hastings algorithm to compute a pseudo-posterior
distribution based on the composite likelihood. Two methodologies for adjusting
the algorithm are presented and their performance on approximating the true
posterior distribution is investigated using simulated data sets and real data
on spatial extremes of rainfall
Conditional ergodicity in infinite dimension
The goal of this paper is to develop a general method to establish
conditional ergodicity of infinite-dimensional Markov chains. Given a Markov
chain in a product space, we aim to understand the ergodic properties of its
conditional distributions given one of the components. Such questions play a
fundamental role in the ergodic theory of nonlinear filters. In the setting of
Harris chains, conditional ergodicity has been established under general
nondegeneracy assumptions. Unfortunately, Markov chains in infinite-dimensional
state spaces are rarely amenable to the classical theory of Harris chains due
to the singularity of their transition probabilities, while topological and
functional methods that have been developed in the ergodic theory of
infinite-dimensional Markov chains are not well suited to the investigation of
conditional distributions. We must therefore develop new measure-theoretic
tools in the ergodic theory of Markov chains that enable the investigation of
conditional ergodicity for infinite dimensional or weak-* ergodic processes. To
this end, we first develop local counterparts of zero-two laws that arise in
the theory of Harris chains. These results give rise to ergodic theorems for
Markov chains that admit asymptotic couplings or that are locally mixing in the
sense of H. F\"{o}llmer, and to a non-Markovian ergodic theorem for stationary
absolutely regular sequences. We proceed to show that local ergodicity is
inherited by conditioning on a nondegenerate observation process. This is used
to prove stability and unique ergodicity of the nonlinear filter. Finally, we
show that our abstract results can be applied to infinite-dimensional Markov
processes that arise in several settings, including dissipative stochastic
partial differential equations, stochastic spin systems and stochastic
differential delay equations.Comment: Published in at http://dx.doi.org/10.1214/13-AOP879 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
The stability of conditional Markov processes and Markov chains in random environments
We consider a discrete time hidden Markov model where the signal is a
stationary Markov chain. When conditioned on the observations, the signal is a
Markov chain in a random environment under the conditional measure. It is shown
that this conditional signal is weakly ergodic when the signal is ergodic and
the observations are nondegenerate. This permits a delicate exchange of the
intersection and supremum of -fields, which is key for the stability of
the nonlinear filter and partially resolves a long-standing gap in the proof of
a result of Kunita [J. Multivariate Anal. 1 (1971) 365--393]. A similar result
is obtained also in the continuous time setting. The proofs are based on an
ergodic theorem for Markov chains in random environments in a general state
space.Comment: Published in at http://dx.doi.org/10.1214/08-AOP448 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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