A study on the theory of identifiability parametric and its applications in new distributions and classes of probability distributions
Probability. Distribution. Identifiability.
Lately, many authors have proposed new classes of distributions, which are modifications of distribution functions that provide rate-of-risk functions taking various forms. Several families proposed in the literature constitute generalizations of probability distributions because, in general, the resulting distribution and the baseline have the same support. It is well known that adding parameters to distribution classes can lead to problems with identifiability and consequently bring complications to the estimation of parameters in the proposed model. This work presented definitions that contribute to the theory of identifiability. As mentioned earlier, some theorems and propositions based on the definitions have been introduced to present a new perspective on the theory. The class of distributions T-G-X [Method for generating distributions and classes of probability distributions: the univariate case, Brito et al. (2019)] was displayed and discussed. It constitutes a multi-baseline extension of the well-known T-X class. Some theorems and propositions based on definitions were introduced to present a new perspective on the theory above. Mixtures involving addition and multiplication operations of distribution functions from the class T-G-X were presented and studied in terms of identifiability.