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September 2013 A decision-theoretic approach for segmental classification
Christopher Yau, Christopher C. Holmes
Ann. Appl. Stat. 7(3): 1814-1835 (September 2013). DOI: 10.1214/13-AOAS657


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 $\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\hat{x}$ under the posterior distribution $\pi(x|y)$? That is, how should you make a prediction of the underlying states? We demonstrate that traditional approaches such as reporting the most probable state sequence or most probable set of marginal predictions can give undesirable classification artefacts and offer limited control over the properties of the prediction. We propose a decision theoretic approach using a novel class of Markov loss functions and report $\hat{x}$ via the principle of minimum expected loss (maximum expected utility). We demonstrate that the sequence of minimum expected loss under the Markov loss function can be enumerated exactly using dynamic programming methods and that it offers flexibility and performance improvements over existing techniques. The result is generic and applicable to any probabilistic model on a sequence, such as Hidden Markov models, change point or product partition models.


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Christopher Yau. Christopher C. Holmes. "A decision-theoretic approach for segmental classification." Ann. Appl. Stat. 7 (3) 1814 - 1835, September 2013.


Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 06237199
MathSciNet: MR3127970
Digital Object Identifier: 10.1214/13-AOAS657

Keywords: Bayesian , decision theory , Segmental classification

Rights: Copyright © 2013 Institute of Mathematical Statistics


Vol.7 • No. 3 • September 2013
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