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B1482
Title: Stein characterizations of non-normalized discrete probability distributions and their applications in statistics Authors:   - ()
Bruno Ebner - Karlsruhe Institute of Technology (Germany)
Franz Nestmann - Karlsruhe Institute of Technology (Germany)
Abstract: From the distributional characterizations that lie at the heart of Stein's method, explicit formulae are derived for the mass functions of discrete probability laws that identify those distributions. These identities are used to develop tools for the solution of statistical problems. The characterizations, and hence the applications built on them, do not require any knowledge about normalization constants of the probability laws. To demonstrate that our statistical methods are sound, we provide comparative simulation studies in goodness-of-fit and parameter estimation problems. In particular, we discuss parameter estimation for discrete exponential-polynomial models, which, generally, are non-normalized.