Estimating the parameters of the generalized KA distribution by applying the expectation maximization algorithm
| Autor: | I. O. Vardiambasis, Theonymphi M. Melesanaki, M.P. Ioannidou, A.M. Maras, Evangelos A. Kokkinos |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Generalization 0211 other engineering and technologies Statistical model 02 engineering and technology 01 natural sciences Data modeling Data set Distribution (mathematics) Approximation error Expectation–maximization algorithm General Earth and Planetary Sciences Applied mathematics Intensity (heat transfer) 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of Applied Remote Sensing. 13:1 |
| ISSN: | 1931-3195 |
| DOI: | 10.1117/1.jrs.13.014518 |
| Popis: | Generalization of the KA distribution is formulated by combining the class A and K distributions; the resulting distribution is termed as generalized KA distribution. It is obtained as a mixture of a generalized Rayleigh and a class A distribution with gamma-distributed mean intensity, and it may be used to describe clutter statistics. Its parameters are estimated by implementing the expectation maximization algorithm. The latter provides estimates in the framework of the maximum likelihood principle, and it is widely used when the data set is incomplete and/or of limited size. The numerical results show that the absolute relative error of the estimated parameters may be |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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