Entropy based derivation of probability distributions: A case study to daily rainfall

By Simon Michael Papalexiou, Demetris Koutsoyiannis. 2012

114 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f0e8b02cd4e3f2fb96262c78c104b6047fa0b98d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. F. Serinaldi
2. Simon Michael Papalexiou
3. Demetris Koutsoyiannis
4. Vijay P. Singh
5. Chris G. Kilsby
6. Khaled H. Hamed
7. Shenglian Guo
8. E. Volpi

In Statistical and Nonlinear Physics

1. Simon Michael Papalexiou
2. F. Serinaldi
3. Demetris Koutsoyiannis
4. Keith Beven

In Statistics and Probability

1. Simon Michael Papalexiou
2. F. Serinaldi
3. Demetris Koutsoyiannis
4. Shenglian Guo
5. Vijay P. Singh
6. Lu Chen
7. Khaled H. Hamed

Citations in the corpus

• Battle of extreme value distributions: A global survey on extreme daily rainfall (2013, result): “[11] To demonstrate this issue, we use results from a previous study [Papalexiou and Koutsoyiannis, 2012] where we analyzed more than 10,000 daily rainfall records and found that both the Burr type XII (BrXII) distribution and the generalized gamma (GG) distribution are very good models for describi”
• A global survey on the seasonal variation of the marginal distribution of daily precipitation (2016, methodology): “In this direction, in a previous study [19] we used the principle of 376 maximum entropy to derive consistent distributions for geophysical random variables.”
• Revisiting long-range dependence in annual precipitation (2018, unknown): “Hurst behaviour also has a strong physical 29 basis, as it is derived from the principle of entropy maximization [Koutsoyiannis, 2011a], a 30 principle which can be used to determine the theoretical probability distribution model for 31 rainfall [Papalexiou and Koutsoyiannis, 2012].”
• Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, methodology): “For marginal distributions of positive variables, we considered a set of distribution families (i.e., Generalized PAPALEXIOU AND SERINALDI 14 of 27 Gamma, Burr types III and XII, Beta of the Second Kind, and the Generalized Standard Gompertz introduced here) whose justification is based on simple cr”