Simulation of the generalized uniform distribution
Abstract
The continuous uniform distribution is one of the simplest models in statistical analysis but it has been the basis for developing new distribution families.This paper presents two generalizations or extensions of the continuous uniform distribution developed by Jose and Krishna (2011); Sankaran and Jayakumar (2016), in both cases the authors added new parameters to generate new distribution families. To identify the flexibility of both generalizations, a simulation of the probability density function, the distribution function, and the hazard rate function was carried out. Besides, the simulation results were compared with other existing distribution functions to identify to which other models the two generalizations fit. The results show that the two generalizations have behaviors similar to Gamma, Beta, Johnson, and Pearson distribution functions.
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References
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