Abstract
This contribution addresses the problem of obtaining reliable statistical inference from meteorological and climatological records. The common practice is to choose a linear model for the time series, then compute confidence intervals (CIs) for its parameters based on the estimated model. It is demonstrated that such CIs may become misleading when the underlying data generating mechanism is nonlinear, while the computer intensive subsampling method provides an attractive alternative (including situations when linear models are entirely out of place, e.g., when constructing CIs for the skewness).
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Gluhovsky, A. (2008). Subsampling Methodology for the Analysis of Nonlinear Atmospheric Time Series. In: Donner, R.V., Barbosa, S.M. (eds) Nonlinear Time Series Analysis in the Geosciences. Lecture Notes in Earth Sciences, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78938-3_1
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DOI: https://doi.org/10.1007/978-3-540-78938-3_1
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