The Bahadur representation for sample quantiles under negatively associated sequence

DOI10.1016/j.spl.2008.03.026zbMath1147.62333OpenAlexW1966757550MaRDI QIDQ952853

Nengxiang Ling

Publication date: 14 November 2008

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2008.03.026

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20)

Related Items

MODERATE DEVIATION PRINCIPLE OF SAMPLE QUANTILES AND ORDER STATISTICS ⋮ On the Bahadur representation of sample quantiles for ψ-mixing sequences and its application ⋮ Berry-Esséen bound of sample quantiles for negatively associated sequence ⋮ Asymptotics for the linear kernel quantile estimator ⋮ Moment inequalities for \(m\)-negatively associated random variables and their applications ⋮ Some improved results on Berry–Esséen bounds for strong mixing random variables and applications ⋮ A remark on the Bahadur representation of sample quantiles for negatively associated sequences ⋮ The Berry-Esséen type bound of sample quantiles for strong mixing sequence ⋮ A general result on complete convergence for weighted sums of linear processes and its statistical applications ⋮ Testing Independence in Linear Process with Non-Normal Innovations ⋮ A Note on the Berry-Esséen Bound of Sample Quantiles for ϕ-mixing Sequence ⋮ The Bahadur representation for sample quantiles under dependent sequence ⋮ The Bahadur representation of sample quantiles for weakly dependent sequences ⋮ On Bahadur representation for sample quantiles under \(\alpha\)-mixing sequence ⋮ The Bahadur representation of sample quantiles for φ-mixing random variables and its application

Cites Work

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• On deviations between empirical and quantile processes for mixing random variables
• Smooth estimate of quantiles under association
• Uniformly asymptotic normality of the regression weighted estimator for negatively associated samples.
• Negative association of random variables, with applications
• The Bahadur representation of sample quantiles for sequences of strongly mixing random variables
• A note on quantile estimation for long-range dependent stochastic processes
• The Bahadur representation for sample quantiles under weak dependence
• Asymptotic properties for estimates of nonparametric regression models based on negatively associated sequences
• On a semiparametric regression model whose errors form a linear process with negatively associated innovations
• A Note on Quantiles in Large Samples