Generalized Error Exponents for Small Sample Universal Hypothesis Testing
Dayu Huang, Sean Meyn · IEEE Transactions on Information Theory · 2013
The small sample universal hypothesis testing problem is investigated in this paper, in which the number of samples$n$is smaller than the number of possible outcomes$m$. The goal of this paper is to find an appropriate criterion to analyze statistical tests in this setting. A suitable model for analysis is the high-dimensional model in which both$n$and$m$increase to infinity, and$n=o(m)$. A new performance criterion based on large deviations analysis is proposed and it generalizes the classical error exponent applicable for large sample problems (in which$m=O(n)$). This generalized error exponent criterion provides insights that are not available from asymptotic consistency or central limit theorem analysis. The following results are established for the uniform null distribution: 1) The best achievable probability of error$P_{e}$decays as$P_{e}=\exp \{-(n^{2}/m) J (1+o(1))\}$for some$J>0$. 2) A class of tests based on separable statistics, including the coincidence-based test, attains the optimal generalized error exponents. 3) Pearson's chi-square test has a zero generalized error exponent and thus its probability of error is asymptotically larger than the optimal test.