Babette Picker | Searcharxiv

arXiv2026

Stochastic analysis for the Dirichlet--Ferguson process

We study a Dirichlet--Ferguson process $\zeta$ on a general phase space. First we reprove the chaos expansion from Peccati (2008), providing an explicit formula for the kernel functions. Then we proceed with developing a Malliavin calculus for $\zeta$. To this end we introduce a gradient, a divergence and a generator which act as linear operators on random variables or random fields and which are linked by some basic formulas such as integration by parts. While this calculus is strongly motivated by Malliavin calculus for isonormal Gaussian processes and the general Poisson process, the strong dependence properties of $\zeta$ require considerably more combinatorial efforts. We apply our theory to identify our generator as the generator of the Fleming--Viot process and to describe the associated Dirichlet form explicitly in terms of the chaos expansion. We also establish the product and chain chain rule for the gradient and an integral representation of the divergence. Finally we give a short direct proof of the Poincar\'e inequality.

math.PR↗

arXiv2023

Stein's Method of Moments

Stein operators allow to characterise probability distributions via differential operators. Based on these characterisations, we develop a new method of point estimation for marginal parameters of strictly stationary and ergodic processes, which we call \emph{Stein's Method of Moments} (SMOM). These SMOM estimators satisfy the desirable classical properties such as consistency and asymptotic normality. As a consequence of the usually simple form of the operator, we obtain explicit estimators in cases where standard methods such as (pseudo-) maximum likelihood estimation require a numerical procedure to calculate the estimate. In addition, with our approach, one can choose from a large class of test functions which allows to improve significantly on the moment estimator. Moreover, for i.i.d.\ observations, we retrieve data-dependent functions that result in asymptotically efficient estimators and give a sequence of explicit SMOM estimators that converge to the maximum likelihood estimator. Our simulation study demonstrates that for a number of important univariate continuous probability distributions our SMOM estimators possess excellent small sample behaviour, often outperforming the maximum likelihood estimator and other widely-used methods in terms of lower bias and mean squared error. We also illustrate the pertinence of our approach on a real data set related to rainfall modelisation.

math.ST↗