Parameter Estimation of Linear Stochastic Differential Equations with Sparse Observations

Parameter Estimation in Stochastic Differential Equations

Financial processes as processes in nature, are subject to stochastic fluctuations. Stochastic differential equations turn out to be an advantageous representation of such noisy, real-world problems, and together with their identification, they play an important role in the sectors of finance, but also in physics and biotechnology. These equations, however, are often hard to represent and to re...

On sequential parameter estimation for some linear stochastic differential equations with time delay

We consider the parameter estimation problem for the scalar di usion type process described by the stochastic equation with time delay

On sequential parameter estimation for some linear stochastic differential equations with time delay

We consider the parameter estimation problem for the scalar di usion type process described by the stochastic equation with time delay

On Guaranteed Parameter Estimation of Stochastic Delay Differential Equations by Noisy Observations

Parameter estimation of high-dimensional linear differential equations

We study the problem of estimating the coefficients in linear ordinary differential equations (ODE’s) with a diverging number of variables when the solutions are observed with noise. The solution trajectories are first smoothed with local polynomial regression and the coefficients are estimated with nonconcave penalty proposed by [4]. Under some regularity and sparsity conditions, we show the p...