Estimating the number of change points in a sequence of independent normal random variables
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Publication:1907916
DOI10.1016/0167-7152(94)00227-YzbMath0839.62015MaRDI QIDQ1907916
Publication date: 11 March 1996
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
weak consistencypenaltylog likelihood functionSchwarz's criterionindependent normal random variablesnumber of change points
Related Items (14)
Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection ⋮ Nonparametric multiple change-point estimators ⋮ Data-driven estimation of change-points with mean shift ⋮ Wild binary segmentation for multiple change-point detection ⋮ Break Detection for a Class of Nonlinear Time Series Models ⋮ Tail-greedy bottom-up data decompositions and fast multiple change-point detection ⋮ Exact posterior distributions and model selection criteria for multiple change-point detection problems ⋮ An estimator of the number of change points based on a weak invariance principle ⋮ On optimal multiple changepoint algorithms for large data ⋮ Information criterion for Gaussian change-point model ⋮ A shape-based cutting and clustering algorithm for multiple change-point detection ⋮ Exact Spike Train Inference Via $\ell_0$ Optimization ⋮ Bayesian Estimation of the Number of Change Points in Simple Linear Regression Models ⋮ Comments on: ``Extensions of some classical methods in change point analysis
Cites Work
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- A limit theorem for the maximum of normalized sums of independent random variables
- Estimating the number of change-points via Schwarz' criterion
- Product partition models for change point problems
- Estimating the dimension of a model
- Using the generalized likelihood ratio statistic for sequential detection of a change-point
- Detection of the number, locations and magnitudes of jumps
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