ASYMPTOTIC STATISTICAL ANALYSIS OF STATIONARY ERGODIC TIME SERIES

Daniil Ryabko 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
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.
Complete list of metadatas

https://hal.inria.fr/hal-00771128
Contributor : Daniil Ryabko <>
Submitted on : Tuesday, January 8, 2013 - 10:14:58 AM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Long-term archiving on : Tuesday, April 9, 2013 - 3:51:15 AM

File

stats.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00771128, version 1

Collections

Citation

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

Share

Metrics

Record views

646

Files downloads

151