Entropy-Based Parameter Estimation for the Four-Parameter Exponential Gamma Distribution
| Autor: | Xiaoyan Song, Songbai Song, Yan Kang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Exponential distribution
010504 meteorology & atmospheric sciences 0208 environmental biotechnology Erlang distribution General Physics and Astronomy maximum likelihood estimation lcsh:Astrophysics 02 engineering and technology 01 natural sciences principle of maximum entropy four-parameter exponential gamma distribution Statistics methods of moments lcsh:QB460-466 Gamma distribution Applied mathematics lcsh:Science 0105 earth and related environmental sciences Mathematics precipitation frequency analysis Estimation theory Principle of maximum entropy Laplace distribution lcsh:QC1-999 020801 environmental engineering Exponential function Maximum entropy probability distribution lcsh:Q lcsh:Physics |
| Zdroj: | Entropy; Volume 19; Issue 5; Pages: 189 Entropy, Vol 19, Iss 5, p 189 (2017) |
| ISSN: | 1099-4300 |
| DOI: | 10.3390/e19050189 |
| Popis: | Two methods based on the principle of maximum entropy (POME), the ordinary entropy method (ENT) and the parameter space expansion method (PSEM), are developed for estimating the parameters of a four-parameter exponential gamma distribution. Using six data sets for annual precipitation at the Weihe River basin in China, the PSEM was applied for estimating parameters for the four-parameter exponential gamma distribution and was compared to the methods of moments (MOM) and of maximum likelihood estimation (MLE). It is shown that PSEM enables the four-parameter exponential distribution to fit the data well, and can further improve the estimation. |
| Databáze: | OpenAIRE |
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