ASYMPTOTIC STATISTICAL ANALYSIS OF STATIONARY ERGODIC TIME SERIES
Abstract
It is shown how to construct asymptotically consistent efficient algorithms for various statistical problems concerning stationary ergodic time series. The considered problems include clustering, hypothesis testing, change-point estimation and others. The presented approach is based on empirical estimates of the distributional distance. Some open problems are also discussed.
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