On central and non-central limit theorems in density estimation for sequences of long-range dependence

DOI10.1016/0304-4149(96)00072-5zbMath0902.62046OpenAlexW1975234525MaRDI QIDQ1272162

Hwai-Chung Ho

Publication date: 23 November 1998

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(96)00072-5

zbMATH Keywords

kernel density estimatorlong-range dependencecentral limit theoremsinstantaneous filter

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10)

Related Items (9)

On nonparametric density estimation for multivariate linear long-memory processes ⋮ A note on asymptotic normality of kernel estimation for linear random fields on \(Z^{2}\) ⋮ How the instability of ranks under long memory affects large-sample inference ⋮ Kernel density estimation for linear processes ⋮ Nonparametric density estimation for linear processes with infinite variance ⋮ Central limit theorems for nonparametric estimators with real-time random variables ⋮ The smoothing dichotomy in nonparametric regression under long‐memory errors ⋮ Nonparametric estimation under long memory dependence ⋮ Practically applicable central limit theorem for spatial statistics

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