Abstract Details
Activity Number:
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570
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Type:
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Invited
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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JASA, Applications and Case Studies
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Abstract - #306987 |
Title:
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Statistical Learning with Time Series Dependence: An Application to Scoring Sleep in Mice
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Author(s):
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Blakeley B. McShane*+ and Shane T. Jensen and Allan I. Pack and Abraham J Wyner
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Companies:
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Northwestern University and The Wharton School, University of Pennsylvania and University of Pennsylvania and The Wharton School, University of Pennsylvania
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Keywords:
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sleep ;
REM ;
classification ;
Markov ;
time series
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Abstract:
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We develop methodology which combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate general and long-term time dependencies in an easily estimable and computationally tractable fashion. We apply our methodology to scoring sleep in mice. As currently-used methods are expensive, invasive, and labor intensive, there is considerable interest in high-throughput automated systems. Previous efforts have been able to differentiate sleep from wakefulness, but they are unable to differentiate the rare and important state of REM sleep from non-REM sleep. Key difficulties in detecting REM are that (i) REM is much rarer than non-REM and wakefulness, (ii) REM looks similar to non-REM in terms of the observed covariates, (iii) the data are noisy, and (iv) the data contain strong time dependence structures crucial for differentiating REM from non-REM. Our new approach (i) shows improved differentiation of REM from non-REM sleep and (ii) accurately estimates aggregate quantities of sleep in our application to video-based sleep scoring of mice.
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