Bahadur-Kiefer theory for sample quantiles of weakly dependent linear processes

DOI10.3150/07-BEJ6086zbMath1232.62073arXivmath/0605282MaRDI QIDQ2469666

Rafał Kulik

Publication date: 6 February 2008

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0605282

zbMATH Keywords

Bahadur representationempirical processeslaw of the iterated logarithmlinear processesstrong approximationsample quantiles

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Strong limit theorems (60F15) Inference from stochastic processes (62M99)

Related Items (9)

Nonparametric estimation of quantiles for a class of stationary processes ⋮ General matching quantiles M-estimation ⋮ Testing Independence in Linear Process with Non-Normal Innovations ⋮ Bahadur representation for \(U\)-quantiles of dependent data ⋮ Bahadur Representation of Linear Kernel Quantile Estimator for Stationary Processes ⋮ The Bahadur representation of sample quantiles for weakly dependent sequences ⋮ Nonparametric estimation of the kernel function of symmetric stable moving average random functions ⋮ Asymptotic distribution of two-sample empirical \(U\)-quantiles with applications to robust tests for shifts in location ⋮ \(U\)-processes, \(U\)-quantile processes and generalized linear statistics of dependent data

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• On Bahadur's Representation of Sample Quantiles
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