34,398 research outputs found
Maximum-likelihood estimation for diffusion processes via closed-form density expansions
This paper proposes a widely applicable method of approximate
maximum-likelihood estimation for multivariate diffusion process from
discretely sampled data. A closed-form asymptotic expansion for transition
density is proposed and accompanied by an algorithm containing only basic and
explicit calculations for delivering any arbitrary order of the expansion. The
likelihood function is thus approximated explicitly and employed in statistical
estimation. The performance of our method is demonstrated by Monte Carlo
simulations from implementing several examples, which represent a wide range of
commonly used diffusion models. The convergence related to the expansion and
the estimation method are theoretically justified using the theory of Watanabe
[Ann. Probab. 15 (1987) 1-39] and Yoshida [J. Japan Statist. Soc. 22 (1992)
139-159] on analysis of the generalized random variables under some standard
sufficient conditions.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1118 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A variational approach to path estimation and parameter inference of hidden diffusion processes
We consider a hidden Markov model, where the signal process, given by a
diffusion, is only indirectly observed through some noisy measurements. The
article develops a variational method for approximating the hidden states of
the signal process given the full set of observations. This, in particular,
leads to systematic approximations of the smoothing densities of the signal
process. The paper then demonstrates how an efficient inference scheme, based
on this variational approach to the approximation of the hidden states, can be
designed to estimate the unknown parameters of stochastic differential
equations. Two examples at the end illustrate the efficacy and the accuracy of
the presented method.Comment: 37 pages, 2 figures, revise
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
Comment: The 2005 Neyman Lecture: Dynamic Indeterminism in Science
Comment on ``The 2005 Neyman Lecture: Dynamic Indeterminism in Science''
[arXiv:0808.0620]Comment: Published in at http://dx.doi.org/10.1214/07-STS246B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Maximum Likelihood Estimation for Single Particle, Passive Microrheology Data with Drift
Volume limitations and low yield thresholds of biological fluids have led to
widespread use of passive microparticle rheology. The mean-squared-displacement
(MSD) statistics of bead position time series (bead paths) are either applied
directly to determine the creep compliance [Xu et al (1998)] or transformed to
determine dynamic storage and loss moduli [Mason & Weitz (1995)]. A prevalent
hurdle arises when there is a non-diffusive experimental drift in the data.
Commensurate with the magnitude of drift relative to diffusive mobility,
quantified by a P\'eclet number, the MSD statistics are distorted, and thus the
path data must be "corrected" for drift. The standard approach is to estimate
and subtract the drift from particle paths, and then calculate MSD statistics.
We present an alternative, parametric approach using maximum likelihood
estimation that simultaneously fits drift and diffusive model parameters from
the path data; the MSD statistics (and consequently the compliance and dynamic
moduli) then follow directly from the best-fit model. We illustrate and compare
both methods on simulated path data over a range of P\'eclet numbers, where
exact answers are known. We choose fractional Brownian motion as the numerical
model because it affords tunable, sub-diffusive MSD statistics consistent with
typical 30 second long, experimental observations of microbeads in several
biological fluids. Finally, we apply and compare both methods on data from
human bronchial epithelial cell culture mucus.Comment: 29 pages, 12 figure
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