Minimax Sequential Testing for Poisson Processes

Fuente: arXiv
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Main Author: Mei, Hongwei
Format: Preprint
Published: 2023
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author Mei, Hongwei
author_facet Mei, Hongwei
contents Suppose we observe a Poisson process in real time for which the intensity may take on two possible values $λ_0$ and $λ_1$. Suppose further that the priori probability of the true intensity is not given. We solve a minimax version of Bayesian problem of sequential testing of two simple hypotheses to minimize a linear combination of the probability of wrong detection and the expected waiting time in the worst scenario of all possible priori distributions. An equivalent characterization for the least favorable distributions is derived and a sufficient condition for the existence is concluded.
format Preprint
id arxiv_https___arxiv_org_abs_2311_04084
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Minimax Sequential Testing for Poisson Processes
Mei, Hongwei
Statistics Theory
Optimization and Control
Suppose we observe a Poisson process in real time for which the intensity may take on two possible values $λ_0$ and $λ_1$. Suppose further that the priori probability of the true intensity is not given. We solve a minimax version of Bayesian problem of sequential testing of two simple hypotheses to minimize a linear combination of the probability of wrong detection and the expected waiting time in the worst scenario of all possible priori distributions. An equivalent characterization for the least favorable distributions is derived and a sufficient condition for the existence is concluded.
title Minimax Sequential Testing for Poisson Processes
topic Statistics Theory
Optimization and Control
url https://arxiv.org/abs/2311.04084