6 papers · 1 filter
Stein's method of moment estimators for local dependency exponential random graph models
Adrian Fischer, Gesine Reinert, Wenkai Xu
Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarante…
New closed-form estimators for discrete distributions
Adrian Fischer
We revisit the problem of parameter estimation for discrete probability distributions with values in . To this end, we adapt a technique called Stein's Method of Mome…
Normal approximation for the posterior in exponential families
Adrian Fischer, Robert E. Gaunt, Gesine Reinert +1
In this paper, we obtain quantitative, non-asymptotic, and data-dependent \textit{Bernstein-von Mises type} bounds on the normal approximation of the posterior distribution in expo…
Stein's Method of Moments
Bruno Ebner, Adrian Fischer, Robert E. Gaunt +2
Stein operators allow to characterise probability distributions via differential operators. Based on these characterisations, we develop a new method of point estimation for margin…
Stein's Method of Moments on the Sphere
Adrian Fischer, Robert E. Gaunt, Yvik Swan
We use Stein characterizations to obtain new moment-type estimators for the parameters of three classical spherical distributions (namely the Fisher-Bingham, the von Mises-Fisher,…
Stein's method of moments for truncated multivariate distributions
Adrian Fischer, Robert E. Gaunt, Yvik Swan
We use Stein characterisations to derive new moment-type estimators for the parameters of several truncated multivariate distributions in the i.i.d. case; we also derive the asympt…