539 research outputs found
A new framework for extracting coarse-grained models from time series with multiscale structure
In many applications it is desirable to infer coarse-grained models from
observational data. The observed process often corresponds only to a few
selected degrees of freedom of a high-dimensional dynamical system with
multiple time scales. In this work we consider the inference problem of
identifying an appropriate coarse-grained model from a single time series of a
multiscale system. It is known that estimators such as the maximum likelihood
estimator or the quadratic variation of the path estimator can be strongly
biased in this setting. Here we present a novel parametric inference
methodology for problems with linear parameter dependency that does not suffer
from this drawback. Furthermore, we demonstrate through a wide spectrum of
examples that our methodology can be used to derive appropriate coarse-grained
models from time series of partial observations of a multiscale system in an
effective and systematic fashion
Fitting Effective Diffusion Models to Data Associated with a "Glassy Potential": Estimation, Classical Inference Procedures and Some Heuristics
A variety of researchers have successfully obtained the parameters of low
dimensional diffusion models using the data that comes out of atomistic
simulations. This naturally raises a variety of questions about efficient
estimation, goodness-of-fit tests, and confidence interval estimation. The
first part of this article uses maximum likelihood estimation to obtain the
parameters of a diffusion model from a scalar time series. I address numerical
issues associated with attempting to realize asymptotic statistics results with
moderate sample sizes in the presence of exact and approximated transition
densities. Approximate transition densities are used because the analytic
solution of a transition density associated with a parametric diffusion model
is often unknown.I am primarily interested in how well the deterministic
transition density expansions of Ait-Sahalia capture the curvature of the
transition density in (idealized) situations that occur when one carries out
simulations in the presence of a "glassy" interaction potential. Accurate
approximation of the curvature of the transition density is desirable because
it can be used to quantify the goodness-of-fit of the model and to calculate
asymptotic confidence intervals of the estimated parameters. The second part of
this paper contributes a heuristic estimation technique for approximating a
nonlinear diffusion model. A "global" nonlinear model is obtained by taking a
batch of time series and applying simple local models to portions of the data.
I demonstrate the technique on a diffusion model with a known transition
density and on data generated by the Stochastic Simulation Algorithm.Comment: 30 pages 10 figures Submitted to SIAM MMS (typos removed and slightly
shortened
Semi-Parametric Drift and Diffusion Estimation for Multiscale Diffusions
We consider the problem of statistical inference for the effective dynamics
of multiscale diffusion processes with (at least) two widely separated
characteristic time scales. More precisely, we seek to determine parameters in
the effective equation describing the dynamics on the longer diffusive time
scale, i.e. in a homogenization framework. We examine the case where both the
drift and the diffusion coefficients in the effective dynamics are
space-dependent and depend on multiple unknown parameters. It is known that
classical estimators, such as Maximum Likelihood and Quadratic Variation of the
Path Estimators, fail to obtain reasonable estimates for parameters in the
effective dynamics when based on observations of the underlying multiscale
diffusion. We propose a novel algorithm for estimating both the drift and
diffusion coefficients in the effective dynamics based on a semi-parametric
framework. We demonstrate by means of extensive numerical simulations of a
number of selected examples that the algorithm performs well when applied to
data from a multiscale diffusion. These examples also illustrate that the
algorithm can be used effectively to obtain accurate and unbiased estimates.Comment: 32 pages, 10 figure
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