Abstract
Asymptotic expansions are derived for the behavior of the optimal sequential test of whether the unknown drift $\mu$ of a Wiener-Levy process is positive or negative for the case where the process has been observed for a long time. The test is optimal in the sense that it is the Bayes test for the problem where we have an a priori normal distribution of $\mu$, the regret for coming to the wrong conclusion is proportional to $|\mu|$, and the cost of observation is constant per unit time. The Bayes procedure is then compared with the best sequential likelihood ratio test.
Citation
John Breakwell. Herman Chernoff. "Sequential Tests for the Mean of a Normal Distribution II (Large $t$)." Ann. Math. Statist. 35 (1) 162 - 173, March, 1964. https://doi.org/10.1214/aoms/1177703738
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