Generalised block bootstrap and its use in meteorology
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. I...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-06-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | http://www.adv-stat-clim-meteorol-oceanogr.net/3/55/2017/ascmo-3-55-2017.pdf |
| Summary: | 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. |
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| ISSN: | 2364-3579 2364-3587 |