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Coverage probability of bootstrap confidence intervals in heavy-tailed frequency models, with application to precipitation data

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    0350698 - UFA-U 2011 RIV AT eng J - Journal Article
    Kyselý, Jan
    Coverage probability of bootstrap confidence intervals in heavy-tailed frequency models, with application to precipitation data.
    Theoretical and Applied Climatology. Roč. 101, 3-4 (2010), s. 345-361. ISSN 0177-798X
    R&D Projects: GA AV ČR KJB300420801
    Institutional research plan: CEZ:AV0Z30420517
    Keywords : bootstrap * extreme value analysis * confidence intervals * heavy-tailed distributions * precipitation amounts
    Subject RIV: DG - Athmosphere Sciences, Meteorology
    Impact factor: 1.684, year: 2010

    The paper compares coverage probabilities of confidence intervals of high quantiles (5-yr to 200-yr return values) constructed by the nonparametric and parametric bootstrap in frequency analysis of heavy-tailed data, typical for maxima of precipitation amounts. The simulation experiments are based on a wide range of models used for precipitation extremes (Generalized Extreme Value, Generalized Pareto, Generalized Logistic, and mixed distributions). We show that both bootstrap methods underestimate the width of the confidence intervals but that the parametric bootstrap is clearly superior to the nonparametric one.
    Permanent Link: http://hdl.handle.net/11104/0190631