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Journal Articles (Research Report) Extremes Year : 2022

Running minimum in the best-choice problem


We consider the best-choice problem for independent (not necessarily iid) observations $X_1, \cdots, X_n$ with the aim of selecting the sample minimum. We show that in this full generality the monotone case of optimal stopping holds and the stopping domain may be defined by the sequence of monotone thresholds. In the iid case we get the universal lower bounds for the success probability. We cast the general problem with independent observations as a variational first-passage problem for the running minimum process which simplifies obtaining the formula for success probability. We illustrate this approach by revisiting the full-information game (where $X_j$'s are iid uniform-$[0,1]$), in particular deriving new representations for the success probability and its limit by $n \rightarrow \infty$. Two explicitly solvable models with discrete $X_j$'s are presented: in the first the distribution is uniform on $\{j,\cdots,n\}$, and in the second the distribution is uniform on $\{1,\cdots, n\}$. These examples are chosen to contrast two situations where the ties vanish or persist in the large-$n$ Poisson limit.
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Dates and versions

hal-03462375 , version 1 (03-12-2021)



Małgorzata Sulkowska, Alexander Gnedin, Patryk Kozieł. Running minimum in the best-choice problem. Extremes, 2022, ⟨10.1007/s10687-022-00457-3⟩. ⟨hal-03462375⟩
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