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Title: Bahadur representation of linear kernel quantile estimator of VaR under mixing assumptions
Authors: Wei, X
Yang, S
Yu, K
Yang, X
Issue Date: 2010
Citation: Journal of Statistical Planning and Inference, 140 pp. 1621 - 1634, 2010
Abstract: Bahadur representation and its applications have attracted a large number of publications and presentations on a wide variety of problems. Mixing dependency is weak enough to describe the dependent structure of random variables, including observations in time series and longitudinal studies. This note proves the Bahadur representation of sample quantiles for strongly mixing random variables (including ½-mixing and Á-mixing) under very weak mixing coe±cients. As application, the asymptotic normality is derived. These results greatly improves those recently reported in literature.
Appears in Collections:Dept of Mathematics Research Papers

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