Generalized Truncated Fr閏het Generated Family Distributions and Their Applications

Understanding a phenomenon from observed data requires contextual and efficient statistical models. Such models are based on probability distributions having sufficiently flexible properties to adapt maximum of situations. Modern examples include the truncated Fréchet generated family. In this paper, we go even further by introducing more general family, version generalized distribution. This generalization involves new shape parameter modulating extreme some central dispersion parameters, as well skewness weight tails. We also investigate main functions stress-strength parameter, diverse functional series expansions, incomplete moments, various entropy measures, theoretical practical parameters estimation, bivariate extensions through use copulas, estimation model parameters. By considering special member family Weibull distribution parent, fit two sets interest, one about waiting times other precipitation. Solid criteria attest that proposed is superior over extended models, including derived former