This tez introduces generalized beta-generated (GBG) distributions.Sub-models include
all classical beta-generated, Kumaraswamy-generated and exponentiated distributions.
They are maximum entropy distributions under three intuitive conditions, which
show that the classical beta generator skewness parameters only control tail entropy and
an additional shape parameter is needed to add entropy to the centre of the parent distribution.
This parameter controls skewness without necessarily differentiating tail weights.
The GBG class also has tractable properties: we present various expansions for moments,
generating function and quantiles. The model parameters are estimated by maximum
likelihood and the usefulness of the new class is illustrated by means of some realdatasets.