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Abstract

This paper introduces a novel class of probability distributions called normal-tangent-G, whose submodels are parsi- monious and bring no additional parameters besides the baseline’s. We demonstrate that these submodels are iden- tifiable as long as the baseline is. We present some properties of the class, including the series representation of its probability density function (pdf) and two special cases. Monte Carlo simulations are carried out to study the behav- ior of the maximum likelihood estimates (MLEs) of the parameters for a particular submodel. We also perform an application of it to a real dataset to exemplify the modelling benefits of the class.

Keywords

Class of probabilistic distributions Identifiable Maximum likelihood Modelling Normal distribution

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How to Cite
Silveira, F., Gomes-Silva, F., Brito, C., Cunha-Filho, M., Jale, J., Gusmão, F., & Xavier-Júnior, S. (2020). The normal-tangent-G class of probabilistic distributions: properties and real data modelling. Pakistan Journal of Statistics and Operation Research, 16(4), 827-838. https://doi.org/10.18187/pjsor.v16i4.3443

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