Parameter estimation in nonlinear stochastic differential equations

Details Parameter estimation in nonlinear stochastic differential equations Parameter estimation in nonlinear stochastic differential equations

2000-01-12

arxiv.org/abs/nlin/0001019v1

Nonlinear Sciences

Adaptation and Self-Organizing Systems

in press: Chaos, Solitons & Fractals

Scientific paper

We discuss the problem of parameter estimation in nonlinear stochastic differential equations based on sampled time series. A central message from the theory of integrating stochastic differential equations is that there exists in general two time scales, i.e. that of integrating these equations and that of sampling. We argue that therefore maximum likelihood estimation is computational extremely expensive. We discuss the relation between maximum likelihood and quasi maximum likelihood estimation. In a simulation study, we compare the quasi maximum likelihood method with an approach for parameter estimation in nonlinear stochastic differential equations that disregards the existence of the two time scales.

Affiliated with

Also associated with

No associations

Rating

Parameter estimation in nonlinear stochastic differential equations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Parameter estimation in nonlinear stochastic differential equations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Parameter estimation in nonlinear stochastic differential equations will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-686515