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# Decision Theory and large deviations for dynamical hypotheses tests: The Neyman-Pearson Lemma, Min-Max and Bayesian tests

• *Corresponding author: Artur O. Lopes

H.H. Ferreira was supported by CAPES-Brazil scholarship. A.O. Lopes and S.R.C. Lopes were partially supported by grant of CNPq-Brazil

• We analyze hypotheses tests using classical results on large deviations to compare two models, each one described by a different Hölder Gibbs probability measure. One main difference to the classical hypothesis tests in Decision Theory is that here the two measures are singular with respect to each other. Among other objectives, we are interested in the decay rate of the wrong decisions probability, when the sample size $n$ goes to infinity. We show a dynamical version of the Neyman-Pearson Lemma displaying the ideal test within a certain class of similar tests. This test becomes exponentially better, compared to other alternative tests, when the sample size goes to infinity. We are able to present the explicit exponential decay rate. We also consider both, the Min-Max and a certain type of Bayesian hypotheses tests. We shall consider these tests in the log likelihood framework by using several tools of Thermodynamic Formalism. Versions of the Stein's Lemma and Chernoff's information are also presented.

Mathematics Subject Classification: 62C20, 62C10, 37D35.

 Citation: • • Figure 1.  Graphs of $P_0$ (in solid line) and $P_1$ (in dashed line) for the functions defined in (3.27). For these plots we consider the data from the example in Section 7

Figure 2.  The large deviation rate function $I_1(\cdot)$ at points $v_1$, $G_1$ and zero, where $G_1 = E - \left(\int \log J_0 \, d \mu_{0} \, - \int \log \, J_1\, \, d\mu_0\, \right)$

Figure 3.  Graph of the function $R(\lambda) = I_0^{E_{\lambda, \lambda}}(0)$, when $0\leq \lambda\leq \lambda_s$, using the stochastic matrix $\mathcal{P}_{j}$, for $j = 0, 1$, from the example in Section 7

Figure 4.  Graphs of the functions $\lambda \to \int \log J_\lambda d \mu_0$ (in dotted line) and $\lambda \to \int \log J_\lambda d \mu_1$ (in dashed and dotted line) together with the graph of the values $E_\lambda$ (in solid line), as a function of $\lambda$, when $0\leq \lambda\leq \lambda_s$. The stochastic matrix $\mathcal{P}_{j}$, for $j = 0, 1$, is from the example in Section 7

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