Activity Number:
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332
- SPEED: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #323822
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View Presentation
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Title:
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A Bayesian Approach for the Segmentation of Series Corrupted by a Functional Part
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Author(s):
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Cristian Meza* and Meili Baragatti and Karine Bertin and Emilie Lebarbier
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Companies:
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CIMFAV-Universidad de Valparaíso and SupAgro-INRA and CIMFAV-Universidad de Valparaíso and AgroParisTech
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Keywords:
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Segmentation ;
Corrupted series ;
Dictionary approach ;
Stochastic search variable selection
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Abstract:
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We propose a Bayesian approach to detect multiple change-points in a piecewise-constant signal corrupted by a functional part corresponding to environmental or experimental disturbances. The piecewise constant part (also called segmentation part) is expressed as the product of a lower triangular matrix by a sparse vector. The functional part is a linear combination of functions from a large dictionary. A Stochastic Search Variable Selection approach is used to obtain sparse estimations of the segmentation parameters (the change-points and the means over the segments) and of the functional part. The performance of our proposed method is assessed using simulation experiments. Applications to two real datasets from geodesy and economy fields are also presented.
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Authors who are presenting talks have a * after their name.