4,059 research outputs found
A class of fast exact Bayesian filters in dynamical models with jumps
In this paper, we focus on the statistical filtering problem in dynamical
models with jumps. When a particular application relies on physical properties
which are modeled by linear and Gaussian probability density functions with
jumps, an usualmethod consists in approximating the optimal Bayesian estimate
(in the sense of the Minimum Mean Square Error (MMSE)) in a linear and Gaussian
Jump Markov State Space System (JMSS). Practical solutions include algorithms
based on numerical approximations or based on Sequential Monte Carlo (SMC)
methods. In this paper, we propose a class of alternative methods which
consists in building statistical models which share the same physical
properties of interest but in which the computation of the optimal MMSE
estimate can be done at a computational cost which is linear in the number of
observations.Comment: 21 pages, 7 figure
Latent Gaussian Count Time Series Modeling
This paper develops theory and methods for the copula modeling of stationary
count time series. The techniques use a latent Gaussian process and a
distributional transformation to construct stationary series with very flexible
correlation features that can have any pre-specified marginal distribution,
including the classical Poisson, generalized Poisson, negative binomial, and
binomial count structures. A Gaussian pseudo-likelihood estimation paradigm,
based only on the mean and autocovariance function of the count series, is
developed via some new Hermite expansions. Particle filtering methods are
studied to approximate the true likelihood of the count series. Here,
connections to hidden Markov models and other copula likelihood approximations
are made. The efficacy of the approach is demonstrated and the methods are used
to analyze a count series containing the annual number of no-hitter baseball
games pitched in major league baseball since 1893
Sequential Bayesian inference for implicit hidden Markov models and current limitations
Hidden Markov models can describe time series arising in various fields of
science, by treating the data as noisy measurements of an arbitrarily complex
Markov process. Sequential Monte Carlo (SMC) methods have become standard tools
to estimate the hidden Markov process given the observations and a fixed
parameter value. We review some of the recent developments allowing the
inclusion of parameter uncertainty as well as model uncertainty. The
shortcomings of the currently available methodology are emphasised from an
algorithmic complexity perspective. The statistical objects of interest for
time series analysis are illustrated on a toy "Lotka-Volterra" model used in
population ecology. Some open challenges are discussed regarding the
scalability of the reviewed methodology to longer time series,
higher-dimensional state spaces and more flexible models.Comment: Review article written for ESAIM: proceedings and surveys. 25 pages,
10 figure
Statistical Inference for Partially Observed Markov Processes via the R Package pomp
Partially observed Markov process (POMP) models, also known as hidden Markov
models or state space models, are ubiquitous tools for time series analysis.
The R package pomp provides a very flexible framework for Monte Carlo
statistical investigations using nonlinear, non-Gaussian POMP models. A range
of modern statistical methods for POMP models have been implemented in this
framework including sequential Monte Carlo, iterated filtering, particle Markov
chain Monte Carlo, approximate Bayesian computation, maximum synthetic
likelihood estimation, nonlinear forecasting, and trajectory matching. In this
paper, we demonstrate the application of these methodologies using some simple
toy problems. We also illustrate the specification of more complex POMP models,
using a nonlinear epidemiological model with a discrete population,
seasonality, and extra-demographic stochasticity. We discuss the specification
of user-defined models and the development of additional methods within the
programming environment provided by pomp.Comment: In press at the Journal of Statistical Software. A version of this
paper is provided at the pomp package website: http://kingaa.github.io/pom
Multilevel ensemble Kalman filtering for spatio-temporal processes
We design and analyse the performance of a multilevel ensemble Kalman filter
method (MLEnKF) for filtering settings where the underlying state-space model
is an infinite-dimensional spatio-temporal process. We consider underlying
models that needs to be simulated by numerical methods, with discretization in
both space and time. The multilevel Monte Carlo (MLMC) sampling strategy,
achieving variance reduction through pairwise coupling of ensemble particles on
neighboring resolutions, is used in the sample-moment step of MLEnKF to produce
an efficient hierarchical filtering method for spatio-temporal models. Under
sufficient regularity, MLEnKF is proven to be more efficient for weak
approximations than EnKF, asymptotically in the large-ensemble and
fine-numerical-resolution limit. Numerical examples support our theoretical
findings.Comment: Version 1: 39 pages, 4 figures.arXiv admin note: substantial text
overlap with arXiv:1608.08558 . Version 2 (this version): 52 pages, 6
figures. Revision primarily of the introduction and the numerical examples
sectio
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