Generalised block bootstrap and its use in meteorology

TL;DR

This paper introduces a generalized block bootstrap method for autocorrelated data, improving statistical tests in meteorology by accurately estimating effective sample sizes and dependency structures.

Contribution

It proposes a new block bootstrap approach that allows for exact calculations and better dependency modeling, applied to temperature data analysis.

Findings

Enhanced bootstrap methodology for autocorrelated data

Improved critical value estimation for dependency tests

Application to temperature data demonstrates effectiveness

Abstract

In an earlier paper Rakonczai et al. (2014), we have emphasized the effective sample size for 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 generalisation of the block bootstrap methodology, 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 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 to the used test statistic. The critical values can be computed by the proposed new block bootstrap simulation, where the block sizes are determined e.g. by fitting a VAR model to the observations. The results are illustrated for models of the used temperature data.