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Hypoelliptic diffusions: filtering and inference from complete and partial observations
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DOI：10.1111/rssb.12307.png)
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Abstract
En 中文
The statistical problem of parameter estimation in partially observed hypoelliptic diffusion processes is naturally occurring in many applications. However, because of the noise structure, where the noise components of the different co-ordinates of the multi-dimensional process operate on different timescales, standard inference tools are ill conditioned. We propose to use a higher order scheme to approximate the likelihood, such that the different timescales are appropriately accounted for. We show consistency and asymptotic normality with non-typical convergence rates. When only partial observations are available, we embed the approximation in a filtering algorithm for the unobserved co-ordinates and use this as a building block in a stochastic approximation expectation-maximization algorithm. We illustrate on simulated data from three models: the harmonic oscillator, the FitzHugh-Nagumo model used to model membrane potential evolution in neuroscience and the synaptic inhibition and excitation model used for determination of neuronal synaptic input.
Keywords:
Approximate maximum likelihood
Hypoelliptic diffusion
1,5 strong order discretization scheme
Parameter estimation
Particle filter
Stochastic approximation expectation-maximization algorithm
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