Poisson intensity estimation with reproducing kernels

Poisson intensity estimation with reproducing kernels

Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity functions exist, especially in high dimensional settings. In this paper we develop a new, computationally tractable Reproducing Kernel Hilbert Space (RKHS) fo...

Reproducing kernels , Bergman kernels , Poisson kernels

Duality by Reproducing Kernels

LetA be a determined or overdetermined elliptic differential operator on a smooth compact manifold X. Write A( ) for the space of solutions of the system Au= 0 in a domain X. Using reproducing kernels related to various Hilbert structures on subspaces of A( ), we show explicit identifications of the dual spaces. To prove the regularity of reproducing kernels up to the boundary of , we specify t...

Refinement of Reproducing Kernels

We continue our recent study on constructing a refinement kernel for a given kernel so that the reproducing kernel Hilbert space associated with the refinement kernel contains that with the original kernel as a subspace. To motivate this study, we first develop a refinement kernel method for learning, which gives an efficient algorithm for updating a learning predictor. Several characterization...

Poisson Intensity Estimation Using Wavelets and the

This article introduces a new method for the estimation of the intensity of an inhomogeneous one-dimensional Poisson process. The Fisz transformation transforms a vector of binned Poisson counts to approximate normality. Theory shows that, asymptotically, the transformed vector is normal and the elements uncorrelated. Hence we can use wavelet shrinkage with a global threshold to estimate the Po...