ASYMPTOTIC STATISTICAL ANALYSIS OF STATIONARY ERGODIC TIME SERIES

Daniil Ryabko (1)

1 SEQUEL - Sequential Learning

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.

Domaines: Théorie [stat.TH]; Machine Learning [stat.ML]; Théorie de l'information [cs.IT]; Théorie de l'information et codage [math.IT]

Daniil Ryabko. ASYMPTOTIC STATISTICAL ANALYSIS OF STATIONARY ERGODIC TIME SERIES. WITMSE 2012, Aug 2012, Amsterdam, Netherlands. ⟨hal-00771128⟩