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A DECISION-THEORETIC APPROACH FOR SEGMENTAL CLASSIFICATION

Abstract:

This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data y and a statistical model π(x, y) of the hidden states x, what should we report as the predict...

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Publication status:
Published

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Publisher copy:
10.1214/13-AOAS657

Authors


More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
Journal:
ANNALS OF APPLIED STATISTICS More from this journal
Volume:
7
Issue:
3
Pages:
1814-1835
Publication date:
2013-09-01
DOI:
EISSN:
1941-7330
ISSN:
1932-6157
Language:
English
Keywords:
Pubs id:
pubs:432104
UUID:
uuid:1b648203-cc0f-47a5-adf8-1d0d97288af9
Local pid:
pubs:432104
Source identifiers:
432104
Deposit date:
2013-11-16

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