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A triangular central limit theorem under a new weak dependence condition

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  • Coulon-Prieur, Clémentine
  • Doukhan, Paul

Abstract

We use a new weak dependence condition from Doukhan and Louhichi (Stoch. Process. Appl. 1999, 84, 313-342) to provide a central limit theorem for triangular arrays; this result applies for linear arrays (as in Peligrad and Utev, Ann. Probab. 1997, 25(1), 443-456) and standard kernel density estimates under weak dependence. This extends on strong mixing and includes non-mixing Markov processes and associated or Gaussian sequences. We use Lindeberg method in Rio (Probab. Theory Related Fields 1996, 104, 255-282).

Suggested Citation

  • Coulon-Prieur, Clémentine & Doukhan, Paul, 2000. "A triangular central limit theorem under a new weak dependence condition," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 61-68, March.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:1:p:61-68
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    References listed on IDEAS

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    1. K. C. Chanda & F. H. Ruymgaart, 1990. "General Linear Processes:A Property Of The Empirical Process Applied To Density And Mode Estimation," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(3), pages 185-199, May.
    2. Doukhan, Paul & Louhichi, Sana, 1999. "A new weak dependence condition and applications to moment inequalities," Stochastic Processes and their Applications, Elsevier, vol. 84(2), pages 313-342, December.
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    Cited by:

    1. Anton Schick & Wolfgang Wefelmeyer, 2008. "Root-n consistency in weighted L 1 -spaces for density estimators of invertible linear processes," Statistical Inference for Stochastic Processes, Springer, vol. 11(3), pages 281-310, October.
    2. Manel Kacem & Stéphane Loisel & Véronique Maume-Deschamps, 2016. "Some mixing properties of conditionally independent processes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(5), pages 1241-1259, March.
    3. Hwang, Eunju & Shin, Dong Wan, 2012. "Strong consistency of the stationary bootstrap under ψ-weak dependence," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 488-495.
    4. Silva-García, V.M. & Flores-Carapia, R. & Rentería-Márquez, C. & Luna-Benoso, B. & Aldape-Pérez, M., 2018. "Substitution box generation using Chaos: An image encryption application," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 123-135.
    5. Bardet, Jean-Marc & Surgailis, Donatas, 2013. "Moment bounds and central limit theorems for Gaussian subordinated arrays," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 457-473.
    6. Hwang, Eunju & Shin, Dong Wan, 2012. "Stationary bootstrap for kernel density estimators under ψ-weak dependence," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1581-1593.
    7. Eunju Hwang & Dong Shin, 2016. "Kernel estimators of mode under $$\psi $$ ψ -weak dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 301-327, April.
    8. Kutta, Tim, 2025. "Approximately mixing time series," Statistics & Probability Letters, Elsevier, vol. 220(C).

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