PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the Uncertainty in Sequential Hypothesis Testing
R Santiago-Mozos, R. Fernandez-Lorenzana, Fernando Perez-Cruz and A Artes-Rodriguez
In: 5th IEEE International Symposium on Biomedical Imaging (ISBI), May 2008, Paris, France.

Abstract

We consider the problem of sequential hypothesis testing when the exact pdfs are not known but instead a set of iid samples are used to describe the hypotheses. We modify the classical test by introducing a likelihood ratio interval which accommodates the uncertainty in the pdfs. The test finishes when the whole likelihood ratio interval crosses one of the thresholds and reduces to the classical test as the number of samples to describe the hypotheses tend to infinity. We illustrate the performance of this test in a medical image application related to tuberculosis diagnosis. We show in this example how the test confidence level can be accurately determined.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4913
Deposited By:Fernando Perez-Cruz
Deposited On:24 March 2009