Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55-66, 2017
https://doi.org/10.5194/ascmo-3-55-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
14 Jun 2017
Generalised block bootstrap and its use in meteorology
László Varga and András Zempléni Department of Probability Theory and Statistics, Eötvös Loránd University, Budapest, Hungary
Abstract. In an earlier paper, Rakonczai et al.(2014) emphasised the importance of investigating the effective sample size in case of autocorrelated data. The simulations were based on the block bootstrap methodology. However, the discreteness of the usual block size did not allow for exact calculations. In this paper we propose a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We relate it to the existing optimisation procedures and apply it to a temperature data set. Our other focus is on statistical tests, where quite often the actual sample size plays an important role, even in the case of relatively large samples. This is especially the case for copulas. These are used for investigating the dependencies among data sets. As in quite a few real applications the time dependence cannot be neglected, we investigated the effect of this phenomenon on the used test statistic. The critical value can be computed by the proposed new block bootstrap simulation, where the block size is determined by fitting a VAR model to the observations. The results are illustrated for models of the used temperature data.

Citation: Varga, L. and Zempléni, A.: Generalised block bootstrap and its use in meteorology, Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55-66, https://doi.org/10.5194/ascmo-3-55-2017, 2017.
Publications Copernicus
Download
Short summary
This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
This paper proposes a new generalisation of the block bootstrap methodology, which allows for...
Share