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Asymptotic Statistical Analysis of Time Series: Clustering, Change Point, and Other Problems
This video was recorded at Large-scale Online Learning and Decision Making (LSOLDM) Workshop, Cumberland Lodge 2012. A method for constructing asymptotically consistent efficient algorithms for various statistical problems concerning stationary ergodic time series is presented. The considered problems include clustering, hypothesis testing, change-point estimation and others. The presented approach is based on empirical estimates ofthe distributional distance. Some open problems are also discussed.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
