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Euclidean Distance Based Permutation Methods in Atmospheric Science

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Book cover Statistical Mining and Data Visualization in Atmospheric Sciences

Abstract

The majority of existing statistical methods inherently involve complex nonmetric analysis spaces due to their least squares regression origin; consequently, the analysis space of such statistical methods is not consistent with the simple metric Euclidean geometry of the data space in question. The statistical methods presented in this paper are consistent with the data spaces in question. These alternative methods depend on exact and approximate permutation procedures for univariate and multivariate data involving cyclic phenomena, autoregressive patterns, covariate residual analyses including most linear model based experimental designs, and linear and nonlinear prediction model evaluations. Specific atmospheric science applications include climate change, Atlantic basin seasonal tropical cyclone predictions, analyses of weather modification experiments, and numerical model evaluations for phenomena such as cumulus clouds, clear-sky surface energy budgets, and mesoscale atmospheric predictions.

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Author information

Authors and Affiliations

  1. Department of Statistics, Colorado State University, Fort Collins, CO, 80523-1877, USA

    Paul W. Mielke Jr. (professor of Statistics)

  2. Department of Sociology, Colorado State University, Fort Collins, CO, 80523-1784, USA

    Kenneth J. Berry (professor of Sociology)

Authors
  1. Paul W. Mielke Jr.
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  2. Kenneth J. Berry
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Editor information

Editors and Affiliations

  1. Desert Research Institute, USA

    Timothy J. Brown

  2. Colorado State University, USA

    Paul W. Mielke Jr.

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Mielke, P.W., Berry, K.J. (2000). Euclidean Distance Based Permutation Methods in Atmospheric Science. In: Brown, T.J., Mielke, P.W. (eds) Statistical Mining and Data Visualization in Atmospheric Sciences. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6581-6_2

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  • DOI: https://doi.org/10.1007/978-1-4757-6581-6_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4974-5

  • Online ISBN: 978-1-4757-6581-6

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