2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.6.3 | ||
| Paper Title | Second-Order Asymptotics of Sequential Hypothesis Testing | ||
| Authors | Yonglong Li, Vincent, Y. F. Tan, National University of Singapore, Singapore | ||
| Session | E.6: Hypothesis Testing II | ||
| Presentation | Lecture | ||
| Track | Detection and Estimation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We consider the classical sequential binary hypothesis testing problem in which there are two hypotheses governed respectively by distributions P 0 and P 1 and we would like to decide which hypothesis is true using a sequential test. It is known from the work of Wald and Wolfowitz that as the expectation of the length of the test grows, the optimal typeI and type-II error exponents approach the relative entropies D(P 1 ∥ P 0 ) and D(P 0 ∥ P 1 ). We refine this result by considering the optimal backoff from the corner point of the achievable exponent region (D(P 1 ∥ P 0 ), D(P 0 ∥ P 1 )) under the expectation constraint on the length of the test (or the sample size). We consider the expectation constraint in which the expectation of the sample size is bounded by n, and under mild conditions, characterize the backoff, also coined second-order asymptotics, precisely. Examples are provided to illustrate our results. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
