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A characterization of the first hitting time of double integral processes to curved boundaries

Part of: Special processes Probabilistic methods, simulation and stochastic differential equations Stochastic processes

Published online by Cambridge University Press:  01 July 2016

Jonathan Touboul  and
Olivier Faugeras
Jonathan Touboul*
Affiliation:
Odyssée Laboratory, INRIA/ENS/ENPC
Olivier Faugeras*
Affiliation:
Odyssée Laboratory, INRIA/ENS/ENPC
*
∗ Postal address: INRIA, Sophia-Antipolis, 2004 route des Lucioles, BP 93 06902, Sophia-Antipolis Cedex, France.
∗ Postal address: INRIA, Sophia-Antipolis, 2004 route des Lucioles, BP 93 06902, Sophia-Antipolis Cedex, France.
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Abstract

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The problem of finding the probability distribution of the first hitting time of a double integral process (DIP) such as the integrated Wiener process (IWP) has been an important and difficult endeavor in stochastic calculus. It has applications in many fields of physics (first exit time of a particle in a noisy force field) or in biology and neuroscience (spike time distribution of an integrate-and-fire neuron with exponentially decaying synaptic current). The only results available are an approximation of the stationary mean crossing time and the distribution of the first hitting time of the IWP to a constant boundary. We generalize these results and find an analytical formula for the first hitting time of the IWP to a continuous piecewise-cubic boundary. We use this formula to approximate the law of the first hitting time of a general DIP to a smooth curved boundary, and we provide an estimation of the convergence of this method. The accuracy of the approximation is computed in the general case for the IWP and the effective calculation of the crossing probability can be carried out through a Monte Carlo method.

Keywords

First hitting timefirst passage timecurved boundaryintegrated Wiener processdouble integral processnumerical computation

MSC classification

Primary: 60G40: Stopping times; optimal stopping problems; gambling theory
Secondary: 60G15: Gaussian processes 60K40: Other physical applications of random processes 65C05: Monte Carlo methods
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 40 , Issue 2 , June 2008 , pp. 501 - 528
Copyright
Copyright © Applied Probability Trust 2008 

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