ASYMPTOTICALLY OPTIMAL HYPOTHESIS-TESTING WITH MEMORY CONSTRAINTS

• 成果类型: Article
• 署名作者: BUCKLEW, JA; NEY, PE
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348132
• 发表日期: 1991
• 页码: 982-998
• 关键词: markov process expectations; large time; additive processes

摘要

The binary hypothesis testing problem of deciding between two Markov chains is formulated under memory constraints. The optimality criterion used is the exponential rate with which the probability of error approaches zero as the sample size tends to infinity. The optimal memory constrained test is shown to be the solution of a set of equations derived from suitable large deviation twistings of the transition matrices under the two hypotheses. A computational algorithm and some examples are given.