![]() However, the maximum likelihood ( MLE) method is not easy to obtain, highly computational and too sensitive to extreme values especially for small samples. The former is the most important method since it leads to efficient parameter estimators that are asymptotically normal. Common methods include, but are not limited to, the maximum likelihood method and the method of moments. for a discussion of some (frequently limited) sets of data. A numerical example based on real data is presented to illustrate the implementation of the proposed procedures.Ī wide variety of methods have been developed to perform parameter estimation see e.g. These methods are compared to two older methods, the maximum likelihood and the conventional method of moments techniques using Monte Carlo simulations. In this article, we estimate the parameters of Kumaraswamy distribution using the previously mentioned methods. To enhance the PWM, self-determined probability-weighted moments was introduced by (Haktanir 1997). The PWM is thought to be less impacted by sampling variability and be more efficient at obtaining robust parameter estimates. In addition to POME, the method of probability-weighted moments ( PWM), was introduced and recommended as an alternative to classical moments. ![]() Since Shannon’s formulation of the entropy theory in 1940 and Jaynes’ discovery of the principle of maximum entropy ( POME) in 1950, entropy applications have proliferated across a wide range of different research areas including hydrological and environmental sciences.
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