Change Point Detection by Sparse Parameter Estimation

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Authors

NEUBAUER Jiří VESELÝ Vítězslav

Year of publication 2011
Type Article in Periodical
Magazine / Source Informatica
MU Faculty or unit

Faculty of Economics and Administration

Citation
Web http://www.mii.lt/informatica/pdf/INFO812.pdf
Field Applied statistics, operation research
Keywords change point detection; overparametrized model; sparse parameter estimation
Description The contribution is focused on change point detection in a one-dimensional stochastic process by sparse parameter estimation from an overparametrized model. A stochastic process with change in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. The mentioned method of change point detection in a stochastic process is compared with several standard statistical methods by simulations.
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