Statistical Methods in the Atmospheric Sciences

AvDaniel S. Wilks

Häftad, Engelska, 2019

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Beskrivning

Statistical Methods in the Atmospheric Sciences, Fourth Edition, continues the tradition of trying to meet the needs of students, researchers and operational practitioners. This updated edition not only includes expanded sections built upon the strengths of the prior edition, but also provides new content where there have been advances in the field, including Bayesian analysis, forecast verification and a new chapter dedicated to ensemble forecasting.

Provides a strong, yet concise, introduction to applied statistics that is specific to atmospheric science
Contains revised and expanded sections on nonparametric tests, test multiplicity and quality uncertainty descriptors
Includes new sections on ANOVA, quantile regression, the lasso and other regularization methods, regression trees, changepoint detection, ensemble forecasting and exponential smoothing

Produktinformation

Utgivningsdatum:2019-06-11
Mått:191 x 235 x 39 mm
Vikt:1 720 g
Format:Häftad
Språk:Engelska
Antal sidor:840
Upplaga:4
Förlag:Elsevier Science
ISBN:9780128158234

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Mer om författaren

Daniel S. Wilks is a Professor Emeritus at Cornell University and has been a Member of the Atmospheric Sciences faculty since 1987. His research focuses on the application of statistical methods for the quantification and analysis of uncertainty in meteorological and climatological data and forecasts. Dr. Wilks has taught courses on statistics in the atmospheric sciences and has been an Author or Coauthor of more than 100 peer-reviewed research articles.

Innehållsförteckning

1. Introduction2. Review of Probability3. Empirical Distributions and Exploratory Data Analysis4. Parametric Probability Distributions5. Frequentist Statistical Inference6. Bayesian Inference7. Statistical Forecasting8. Ensemble Forecasting9. Forecast Verification10. Time Series11. Matrix Algebra and Random Matrices12. Multivariate Normal Distribution13. Principal Component (EOF) Analysis14. Linear multivariate analysis of vector pairs: CCA, MCA, and RA15. Discrimination and Classification16. Cluster Analysis