학술논문

Dynamical hypothesis tests and Decision Theory for Gibbs distributions
Document Type
Working Paper
Source
Subject
Mathematics - Statistics Theory
Mathematics - Dynamical Systems
Mathematics - Probability
37D35, 62C20, 62C10
Language
Abstract
We consider the problem of testing for two Gibbs probabilities $\mu_0$ and $\mu_1$ defined for a dynamical system $(\Omega,T)$. Due to the fact that in general full orbits are not observable or computable, one needs to restrict to subclasses of tests defined by a finite time series $h(x_0), h(x_1)=h(T(x_0)),..., h(x_n)=h(T^n(x_0))$, $x_0\in \Omega$, $n\ge 0$, where $h:\Omega\to\mathbb R$ denotes a suitable measurable function. We determine in each class the Neyman-Pearson tests, the minimax tests, and the Bayes solutions, and show the asymptotic decay of their risk functions, as $n\to\infty$. In the case of $\Omega$ being a symbolic space, for each $n\in \mathbb{N}$, these optimal tests rely on the information of the measures for cylinder sets of size $n$.