Statistical inference based on band-limited kernels: Rational-infinitely divisible distributions and beyond

This paper investigates the problem of statistical inference for a mixture distribution consisting of a discrete and a continuous component, with a particular focus on the class of rational-infinitely divisible distributions.

We consider non-parametric estimation of both components of the mixture as well as the quasi-L{é}vy measure, assuming that the mixture belongs to the class of rational-infinitely divisible distributions.

We propose an estimation framework based on band-limited kernels, which are the functions characterized by compactly supported Fourier transform.

Under mild assumptions, the proposed estimators are theoretically shown to achieve polynomial (and in some cases even almost parametric) convergence rates.

Finally, we demonstrate the numerical performance of the algorithm on simulated examples.