A genetic estimation algorithm for parameters of stochastic ordinary differential equations

by: Jamie Alcock, Kevin Burrage

Computational Statistics & Data Analysis, Vol. 47, No. 2. (1 September 2004), pp. 255-275.

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Abstract

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market.

@article{citeulike:379802, abstract = {A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market.}, author = {Alcock, Jamie and Burrage, Kevin }, citeulike-article-id = {379802}, doi = {10.1016/j.csda.2003.11.025}, journal = {Computational Statistics \& Data Analysis}, keywords = {differential, dynamical, equations, estimation, nonlinear, parameter, robust, sde, stochastic}, month = {September}, number = {2}, pages = {255--275}, posted-at = {2005-11-03 21:51:34}, priority = {5}, title = {A genetic estimation algorithm for parameters of stochastic ordinary differential equations}, url = {http://dx.doi.org/10.1016/j.csda.2003.11.025}, volume = {47}, year = {2004} }

RIS record

TY - JOUR ID - citeulike:379802 L3 - citeulike-article-id:379802 TI - A genetic estimation algorithm for parameters of stochastic ordinary differential equations JF - Computational Statistics & Data Analysis VL - 47 IS - 2 SP - 255 EP - 275 N2 - A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. KW - differential KW - dynamical KW - equations KW - estimation KW - nonlinear KW - parameter KW - robust KW - sde KW - stochastic AU - Alcock, Jamie AU - Burrage, Kevin PY - 2004/09/01/ UR - http://dx.doi.org/10.1016/j.csda.2003.11.025 ER -

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