399 research outputs found
Transition density and simulated likelihood estimation for time-inhomogeneous diffusions
We propose a method to estimate the transition density of a non-linear time-inhomogeneous diffusion. Expressing the transition density as a functional of a Brownian bridge, allows us to estimate the density through Monte Carlo simulations with any level of precision. We show how these transition density estimates can be effectively used to estimate the parameters of the time-inhomogeneous diffusion and the conditional moments of the process. In this paper we prove that our method is asymptotically equivalent to the maximum likelihood estimator and more reliable than the closed-form approximation approach largely used in the literature.info:eu-repo/semantics/publishedVersio
A selective overview of nonparametric methods in financial econometrics
This paper gives a brief overview on the nonparametric techniques that are
useful for financial econometric problems. The problems include estimation and
inferences of instantaneous returns and volatility functions of
time-homogeneous and time-dependent diffusion processes, and estimation of
transition densities and state price densities. We first briefly describe the
problems and then outline main techniques and main results. Some useful
probabilistic aspects of diffusion processes are also briefly summarized to
facilitate our presentation and applications.Comment: 32 pages include 7 figure
Estimation in discretely observed diffusions killed at a threshold
Parameter estimation in diffusion processes from discrete observations up to
a first-hitting time is clearly of practical relevance, but does not seem to
have been studied so far. In neuroscience, many models for the membrane
potential evolution involve the presence of an upper threshold. Data are
modeled as discretely observed diffusions which are killed when the threshold
is reached. Statistical inference is often based on the misspecified likelihood
ignoring the presence of the threshold causing severe bias, e.g. the bias
incurred in the drift parameters of the Ornstein-Uhlenbeck model for biological
relevant parameters can be up to 25-100%. We calculate or approximate the
likelihood function of the killed process. When estimating from a single
trajectory, considerable bias may still be present, and the distribution of the
estimates can be heavily skewed and with a huge variance. Parametric bootstrap
is effective in correcting the bias. Standard asymptotic results do not apply,
but consistency and asymptotic normality may be recovered when multiple
trajectories are observed, if the mean first-passage time through the threshold
is finite. Numerical examples illustrate the results and an experimental data
set of intracellular recordings of the membrane potential of a motoneuron is
analyzed.Comment: 29 pages, 5 figure
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Stochastic differential equations (SDEs) or diffusions are continuous-valued
continuous-time stochastic processes widely used in the applied and
mathematical sciences. Simulating paths from these processes is usually an
intractable problem, and typically involves time-discretization approximations.
We propose an exact Markov chain Monte Carlo sampling algorithm that involves
no such time-discretization error. Our sampler is applicable to the problem of
prior simulation from an SDE, posterior simulation conditioned on noisy
observations, as well as parameter inference given noisy observations. Our work
recasts an existing rejection sampling algorithm for a class of diffusions as a
latent variable model, and then derives an auxiliary variable Gibbs sampling
algorithm that targets the associated joint distribution. At a high level, the
resulting algorithm involves two steps: simulating a random grid of times from
an inhomogeneous Poisson process, and updating the SDE trajectory conditioned
on this grid. Our work allows the vast literature of Monte Carlo sampling
algorithms from the Gaussian process literature to be brought to bear to
applications involving diffusions. We study our method on synthetic and real
datasets, where we demonstrate superior performance over competing methods.Comment: 37 pages, 13 figure
Approximation of epidemic models by diffusion processes and their statistical inference
Multidimensional continuous-time Markov jump processes on
form a usual set-up for modeling -like epidemics. However,
when facing incomplete epidemic data, inference based on is not easy
to be achieved. Here, we start building a new framework for the estimation of
key parameters of epidemic models based on statistics of diffusion processes
approximating . First, \previous results on the approximation of
density-dependent -like models by diffusion processes with small diffusion
coefficient , where is the population size, are
generalized to non-autonomous systems. Second, our previous inference results
on discretely observed diffusion processes with small diffusion coefficient are
extended to time-dependent diffusions. Consistent and asymptotically Gaussian
estimates are obtained for a fixed number of observations, which
corresponds to the epidemic context, and for . A
correction term, which yields better estimates non asymptotically, is also
included. Finally, performances and robustness of our estimators with respect
to various parameters such as (the basic reproduction number), ,
are investigated on simulations. Two models, and , corresponding to
single and recurrent outbreaks, respectively, are used to simulate data. The
findings indicate that our estimators have good asymptotic properties and
behave noticeably well for realistic numbers of observations and population
sizes. This study lays the foundations of a generic inference method currently
under extension to incompletely observed epidemic data. Indeed, contrary to the
majority of current inference techniques for partially observed processes,
which necessitates computer intensive simulations, our method being mostly an
analytical approach requires only the classical optimization steps.Comment: 30 pages, 10 figure
Joint Modelling of Gas and Electricity spot prices
The recent liberalization of the electricity and gas markets has resulted in
the growth of energy exchanges and modelling problems. In this paper, we
modelize jointly gas and electricity spot prices using a mean-reverting model
which fits the correlations structures for the two commodities. The dynamics
are based on Ornstein processes with parameterized diffusion coefficients.
Moreover, using the empirical distributions of the spot prices, we derive a
class of such parameterized diffusions which captures the most salient
statistical properties: stationarity, spikes and heavy-tailed distributions.
The associated calibration procedure is based on standard and efficient
statistical tools. We calibrate the model on French market for electricity and
on UK market for gas, and then simulate some trajectories which reproduce well
the observed prices behavior. Finally, we illustrate the importance of the
correlation structure and of the presence of spikes by measuring the risk on a
power plant portfolio
Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods
Parameter estimation in multidimensional diffusion models with only one
coordinate observed is highly relevant in many biological applications, but a
statistically difficult problem. In neuroscience, the membrane potential
evolution in single neurons can be measured at high frequency, but biophysical
realistic models have to include the unobserved dynamics of ion channels. One
such model is the stochastic Morris-Lecar model, defined by a nonlinear
two-dimensional stochastic differential equation. The coordinates are coupled,
that is, the unobserved coordinate is nonautonomous, the model exhibits
oscillations to mimic the spiking behavior, which means it is not of
gradient-type, and the measurement noise from intracellular recordings is
typically negligible. Therefore, the hidden Markov model framework is
degenerate, and available methods break down. The main contributions of this
paper are an approach to estimate in this ill-posed situation and nonasymptotic
convergence results for the method. Specifically, we propose a sequential Monte
Carlo particle filter algorithm to impute the unobserved coordinate, and then
estimate parameters maximizing a pseudo-likelihood through a stochastic version
of the Expectation-Maximization algorithm. It turns out that even the rate
scaling parameter governing the opening and closing of ion channels of the
unobserved coordinate can be reasonably estimated. An experimental data set of
intracellular recordings of the membrane potential of a spinal motoneuron of a
red-eared turtle is analyzed, and the performance is further evaluated in a
simulation study.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS729 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Markov chain Monte Carlo for exact inference for diffusions
We develop exact Markov chain Monte Carlo methods for discretely-sampled,
directly and indirectly observed diffusions. The qualification "exact" refers
to the fact that the invariant and limiting distribution of the Markov chains
is the posterior distribution of the parameters free of any discretisation
error. The class of processes to which our methods directly apply are those
which can be simulated using the most general to date exact simulation
algorithm. The article introduces various methods to boost the performance of
the basic scheme, including reparametrisations and auxiliary Poisson sampling.
We contrast both theoretically and empirically how this new approach compares
to irreducible high frequency imputation, which is the state-of-the-art
alternative for the class of processes we consider, and we uncover intriguing
connections. All methods discussed in the article are tested on typical
examples.Comment: 23 pages, 6 Figures, 3 Table
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
- …