About the Lindeberg method for strongly mixing sequences

Emmanuel Rio

ESAIM: Probability and Statistics (1997)

  • Volume: 1, page 35-61
  • ISSN: 1292-8100

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Rio, Emmanuel. "About the Lindeberg method for strongly mixing sequences." ESAIM: Probability and Statistics 1 (1997): 35-61. <http://eudml.org/doc/104240>.

@article{Rio1997,
author = {Rio, Emmanuel},
journal = {ESAIM: Probability and Statistics},
keywords = {deviation inequalities; concentration of measure; logarithmic Sobolev inequalities; empirical processes; central limit theorem; strongly mixing triangular arrays; Lévy distance},
language = {eng},
pages = {35-61},
publisher = {EDP Sciences},
title = {About the Lindeberg method for strongly mixing sequences},
url = {http://eudml.org/doc/104240},
volume = {1},
year = {1997},
}

TY - JOUR
AU - Rio, Emmanuel
TI - About the Lindeberg method for strongly mixing sequences
JO - ESAIM: Probability and Statistics
PY - 1997
PB - EDP Sciences
VL - 1
SP - 35
EP - 61
LA - eng
KW - deviation inequalities; concentration of measure; logarithmic Sobolev inequalities; empirical processes; central limit theorem; strongly mixing triangular arrays; Lévy distance
UR - http://eudml.org/doc/104240
ER -

References

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Citations in EuDML Documents

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  1. Jean-Marc Bardet, Paul Doukhan, Gabriel Lang, Nicolas Ragache, Dependent Lindeberg central limit theorem and some applications
  2. Clémentine Prieur, Density estimation for one-dimensional dynamical systems
  3. Clémentine Prieur, Density Estimation for One-Dimensional Dynamical Systems
  4. Jérôme Dedecker, Emmanuel Rio, On mean central limit theorems for stationary sequences
  5. Paul Doukhan, José R. León, Asymptotics for the L p -deviation of the variance estimator under diffusion
  6. Michael H. Neumann, A central limit theorem for triangular arrays of weakly dependent random variables, with applications in statistics
  7. Paul Doukhan, José R. León, Asymptotics for the -deviation of the variance estimator under diffusion
  8. Nadine Guillotin-Plantard, Clémentine Prieur, Central limit theorem for sampled sums of dependent random variables

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