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Kernel type smoothed quantile estimation under long memory

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Abstract

This paper studies nonparametric kernel type (smoothed) estimation of quantiles for long memory stationary sequences. The uniform strong consistency and asymptotic normality of the estimates with rates are established. Finite sample behaviors are investigated in a small Monte Carlo simulation study.

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Authors and Affiliations

  1. Department of Mathematics, Institute of Mathematical Science, Nanjing University, Nanjing, 210093, People’s Republic of China

    Lihong Wang

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  1. Lihong Wang
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Correspondence to Lihong Wang.

Additional information

Research supported by NSFC under grants No. 10501020, No. 10671089 and by SRF for ROCS, SEM Grant of Lihong Wang at Nanjing University.

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Wang, L. Kernel type smoothed quantile estimation under long memory. Stat Papers 51, 57–67 (2010). https://doi.org/10.1007/s00362-007-0115-y

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