Modeling Spectral–Temporal Data From Point Source Events

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2011-01-01
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Reising, Monica
Morris, Max
Vardeman, Stephen
Higbee, Shawn
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Vardeman, Stephen
University Professor Emeritus
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Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Abstract

In recent years, a great deal of effort has been invested in developing sensors to detect, locate, and identify “energetic” electromagnetic events. When observed through one type of imaging spectrometer, these events produce a data record that contains complete spectral and temporal information over the event’s evolution. This article describes the development of a statistical model for the data produced by a particular spectral–temporal sensor. While the application is unique in some ways, this approach to model building may be useful in other related contexts. Several plots, estimated parameters, and some additional details for an equation are provided in the Appendix which is available as supplementary material online.

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This article is published as "Modeling spectral-temporal data from point source events." Technometrics, 2011. Vol. 53, No. 2, pp. 183-195, DOI:10.1198/TECH.2011.09014. With Monica Reising, Max Morris, and Shawn Higbee.

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