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Pointwise convergence rates and central limit theorems for kernel density estimators in linear processes

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  • Schick, Anton
  • Wefelmeyer, Wolfgang

Abstract

Convergence rates and central limit theorems for kernel estimators of the stationary density of a linear process have been obtained under the assumption that the innovation density is smooth (Lipschitz). We show that smoothness is not required. For example, it suffices that the innovation density has bounded variation.

Suggested Citation

  • Schick, Anton & Wefelmeyer, Wolfgang, 2006. "Pointwise convergence rates and central limit theorems for kernel density estimators in linear processes," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1756-1760, October.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:16:p:1756-1760
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    References listed on IDEAS

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    1. Tran, Lanh Tat, 1992. "Kernel density estimation for linear processes," Stochastic Processes and their Applications, Elsevier, vol. 41(2), pages 281-296, June.
    2. Marc Hallin & Lanh T. Tran, 1996. "Kernel density estimation for linear processes: asymptotic normality and bandwidth selection," ULB Institutional Repository 2013/2055, ULB -- Universite Libre de Bruxelles.
    3. Marc Hallin & Lanh Tran, 1996. "Kernel density estimation for linear processes: Asymptotic normality and optimal bandwidth derivation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(3), pages 429-449, September.
    4. Zudi Lu, 2001. "Asymptotic Normality of Kernel Density Estimators under Dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(3), pages 447-468, September.
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    Cited by:

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    2. 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.

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