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Confidence intervals for quantile estimation using Jackknife techniques

  • Published:2008-01-09 Issue:4 Volume:23 Page:573-585
  • ISSN:0943-4062
  • Container-title:Computational Statistics
  • language:en
  • Short-container-title:Comput Stat

Author:

Román-Montoya Y.,Rueda M.,Arcos A.

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference34 articles.

1. Binder DA (1982). Non-parametric Bayesian models for samples from finite populations. J R Stat Soc Ser B 44: 388–393

2. Bloznelis M and Götze F (2002). An edgeworth expansion for symmetric finite population statistics. Ann Probab 30(3): 1238–1265

3. Chambers RL and Dunstan R (1986). Estimating distribution functions from survey data. Biometrika 73: 597–604

4. Chaudhuri A and Vos JWE (1988). Unified theory and strategies of survey sampling. North-Holland, Amsterdam

5. DiCiccio J and Efron B (1996). Bootstrap confidence intervals. Stat Sci 11(3): 189–228

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1. Assessing the performance of confidence intervals for high quantiles of Burr XII and Inverse Burr mixtures;Communications in Statistics - Simulation and Computation;2020-04-02

2. The use of bias correction versus the Jackknife when testing the mean reversion and long term mean parameters in continuous time models;Monte Carlo Methods and Applications;2017-01-01

3. Evaluation of coded aperture radiation detectors using a Bayesian approach;Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment;2016-12

4. Improvement of Source Number Estimation Method for Single Channel Signal;PLOS ONE;2016-10-13

5. Jackknifed Liu estimator in linear regression models;Wuhan University Journal of Natural Sciences;2013-08
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