Bahadur representation of linear kernel quantile estimator of VaR under \(\alpha \)-mixing assumptions

DOI10.1016/j.jspi.2010.01.002zbMath1184.62056OpenAlexW2050858914MaRDI QIDQ963848

Xin Yang, Xianglan Wei, Shan-chao Yang, Guo-Dong Xing, Ke-ming Yu

Publication date: 14 April 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.01.002

zbMATH Keywords

Bahadur representationmean square errorstrong consistencykernel quantile estimator\(\alpha \)-mixingVaR

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (12)

A kernel nonparametric quantile estimator for right-censored competing risks data ⋮ Uniformly strong consistency and Berry-Esseen bound of frequency polygons for α-mixing samples ⋮ Asymptotics for the linear kernel quantile estimator ⋮ Frequency polygon estimation of density function for dependent samples ⋮ Some improved results on Berry–Esséen bounds for strong mixing random variables and applications ⋮ Uniform strong consistency of histogram density estimation for dependent process ⋮ The Berry-Esséen type bound of sample quantiles for strong mixing sequence ⋮ Bahadur Representation of Linear Kernel Quantile Estimator for Stationary Processes ⋮ The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data ⋮ Wavelet estimation in heteroscedastic regression models with \(\alpha\)-mixing random errors ⋮ On Bahadur representation for sample quantiles under \(\alpha\)-mixing sequence ⋮ The Bahadur representation of sample quantiles for φ-mixing random variables and its application

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