Given a sample from a discretely observed compound Poisson process, we consider estimation of the density of the jump sizes. We propose a kernel type nonparametric density estimator and study its… (More)

We study the problem of non-parametric Bayesian estimation of the intensity function of a Poisson point process. The observations are assumed to be n independent realisations of a Poisson point… (More)

Given a sample fromadiscretely observed compoundPoissonprocess,we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach… (More)

We consider non-parametric Bayesian estimation of the drift coefficient of a one-dimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under… (More)

In this paper, we study application of Le Cam's one-step method to parameter estimation in ordinary differential equation models. This computationally simple technique can serve as an alternative to… (More)

We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interval. As a prior on… (More)

According to both domain expertise knowledge and empirical evidence, wavelet coefficients of real signals typically exhibit clustering patterns, in that they contain connected regions of coefficients… (More)

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons.… (More)

Let X1, . . . , Xn be i.i.d. observations, where Xi = Yi + Zi and Yi and Zi are independent. Assume that unobservable Y ’s are distributed as a random variable UV, where U has a Bernoulli… (More)

We derive the posteror contraction rate for non-parametric Bayesian estimation of a deterministic dispersion coefficient of a linear stochastic differential equation.