Abstract
The Pareto distribution has been introduced in Chapter 19. Also discussed there is a brief review of literature and methods of estimating its parameters. Further elaboration of the distribution is given in Chapter 20. Methods of parameter estimation were reviewed by Hosking and Wallis (1987). The methods of moments (MOM), maximum likelihood estimation (MLE) and probability weighted moments (PWM) were included in the review. Guo and Singh (1992) and Singh and Guo (1995) employed the principle of maximum entropy (POME) to develop a new competitive method of parameter estimation (Singh and Rajagopal, 1986) for the 3-parameter generalized Pareto (GP3) distribution and compared it with MOM, MLE and PWM using Monte Carlo simulated data The parameter estimates yielded by POME were either superior or comparable for high skewness.
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References
Guo, H. and Singh, V.P., 1992. A comparative evaluation of estimators of Pareto distribution by Monte Carlo simulation. Technical Report WRR25, Water Resources Program, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana.
Hosking, J. R. M. and Wallis, J. R., 1987. Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, Vol. 29, No. 3, pp. 339–349.
Singh, V.P. and Guo, H., 1995. Parameter estimation for 3-parameter generalized Pareto distribution by the principle of maximum entropy (POME). Hydrological Sciences Journal, Vol. 40, No. 2, pp. 165–181.
Singh, V. P. and Rajagopal, A. K., 1986. A new method of parameter estimation for hydrologic frequency analysis. Hydrological Science and Technology, Vol. 2, No. 3, pp. 33–40.
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© 1998 Springer Science+Business Media Dordrecht
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Singh, V.P. (1998). Three-Parameter Generalized Pareto Distribution. In: Entropy-Based Parameter Estimation in Hydrology. Water Science and Technology Library, vol 30. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1431-0_21
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DOI: https://doi.org/10.1007/978-94-017-1431-0_21
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