JSM 2005 - Toronto

Abstract #303796

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 353
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #303796
Title: Statistics Issues in Combining Multiple Sensors
Author(s): Carol Y. Lin*+ and Lance A. Waller and Robert Lyles
Companies: Emory University and Emory University and Emory University
Address: 6 Vivian Lane Apt10, Atlanta, GA, 30305, United States
Keywords: diagnosis ; multiple tests ; correlations ; sensitivity ; specificity ; optimal
Abstract:

Combining individual tests or sensors in a detection system often provides better diagnostic performance. Optimal decision-theoretic combinations exist. However, when designing a detection system, being cost-effective is important, particularly when combining a set of sensors with heterogeneous individual cost and performance. We consider using expected cost of all decisions, constrained by the budget, to select an optimal system from systems with various combinations of different types of sensors. We further examine system performance with various numbers of sensors and allow correlations between individual sensors. The results suggest that increasing the number of sensors increases the combined probability of detection (sensitivity) and decreases the false-alarm rates (1-specificity), while increasing correlation of subunit sensors decreases the combined probability of detection, which increases the false-alarm rate. Furthermore, the magnitude of decline increases with the number of subunit sensors. To illustrate, we consider a network of air pollution monitors in Boston consisting of both expensive and accurate sensors and inexpensive and less-accurate sensors.

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Revised March 2005