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Data Selection in Binary Hypothesis Testing (2003)
BibTeX
@MISC{Iv03dataselection,
author = {Charles K. Sestok Iv and Charles K. Sestok Iv},
title = {Data Selection in Binary Hypothesis Testing},
year = {2003}
}
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Abstract
Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate perfomance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or prohibitively costly. A potential technique to cope with such situations is data selection, where a subset of the measurements that can be collected and processed in a cost-effective manner is used as input to the signal processing algorithm. Careful evaluation of the selection procedure is important, since the probabilistic description of distinct data subsets can vary significantly. An algorithm designed for the probabilistic description of a poorly chosen data subset can lose much of the potential performance available to a well-chosen subset. This thesis considers algorithms for data selection combined with binary hypothesis testing. We develop models for data selection in several cases, considering both random and deterministic approaches. Our considerations are divided into two classes
Keyphrases
data selection binary hypothesis testing probabilistic description statistical signal processing algorithm distinct data subset poorly chosen data subset certain situation probabilistic model available measurement ultimate perfomance selection procedure several case potential technique careful evaluation potential performance assumed model well-chosen subset binary hypothesis deterministic approach signal processing algorithm cost-effective manner
