Summary Of Four Generalized Exponential Models (GEM) For Continuous Probability Distributions

2008/01/18 by Francis J. O'Brien Jr, O'Brien, Francis J.
Mathematics · #FOS: Mathematics #General Mathematics (math.GM) #math.GM

paper · pdf · doi:10.48550/arxiv.0801.2941

66 pages v5; minor typos corrected and formu;and reference formulas added

arxiv created 2014/05/08 · arxiv updated 2014/05/09

Abstract

Four new probability models are derived which generalize the common univariate continuous distributions. Classical distributional measures are derived from Hoel, et al., Introduction to Probability Theory, 1971. Measures include probability density function, moments generating function, cumulative distribution function,derivatives, inverse distributions, skewness, kurtosis, change of variable distributions, log distributions. Maximum likelihood estimation technique is briefly outlined. Appendices describe applications. Errata/addenda sheet included.

Related

• Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach
• A new method for constructing continuous distributions on the unit interval
• Maximum Likelihood Estimation for q-Exponential (Tsallis) Distributions
• On Bivariate Generalized Exponential-Power Series Class of Distributions
• Power new generalized class of Kavya-Manoharan distributions with an application to exponential distribution