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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