6 accepted papers
We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on sequentially observed data whose distribution belongs to the exponential family. The aim is to design a scheme which minimizes the expected number of used samples while limiting the detection and…
We address the problem of robust network localization in realistic mixed LOS/NLOS environments. We make use of the fact that the bias of range measurement errors is not only non-negative but also sparse when LOS dominates, which has been long overlooked in the existing literature. To exploit these t…
Jointly testing multiple hypotheses and estimating a random parameter of the underlying model is investigated in a sequential setup. The optimal scheme is designed such that it minimizes the expected number of used samples while keeping the probabilities of falsely rejecting a hypothesis and the mea…
The problem of deciding whether a given set of data points forms one cluster or two clusters is investigated from a robust hypothesis testing perspective. It is assumed that a clustering algorithm exists that for both cases calculates cluster assignments and estimates of the corresponding probabilit…
The paper deals with minimax optimal statistical tests for two composite hypotheses, where each hypothesis is defined by a nonparametric uncertainty set of feasible distributions. It is shown that for every pair of uncertainty sets of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli…
The problem of sequential multiple hypothesis testing in a distributed sensor network is considered and two algorithms are proposed: the Consensus + Innovations Matrix Sequential Probability Ratio Test (CIMSPRT for multiple simple hypotheses and the robust Least-Favorable-Density- CIMSPRT for hypoth…