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Multiple Change Point Detection by Sparse Parameter Estimation

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Abstract

Abstract. The contribution is focused on multiple change point detection in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. Stochastic process with changes in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. Some properties of mentioned method are studied by simulations

Topics: multiple change point detection, overparametrized model, sparse
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.2199
Provided by: CiteSeerX
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