Abstract

Many flexible families of positive random variables exhibit non-closed

forms of the density and distribution functions and this feature is considered

unappealing for modelling purposes. However, such families are often characterized by a simple expression of the corresponding Laplace transform. Rely-

ing on the Laplace transform, we propose to carry out parameter estimation

and goodness-of-fit testing for a general class of non-standard laws. We suggest

a novel data-driven inferential technique, providing parameter estimators and

goodness-of-fit tests, whose large-sample properties are derived. The implementation of the method is specifically considered for the positive stable and Tweedie

distributions. A Monte Carlo study shows good finite-sample performance of the

proposed technique for such laws.

Information

Preprint No.SS-2023-0393
Manuscript IDSS-2023-0393
Complete AuthorsLucio Barabesi, Antonio Di Noia, Marzia Marcheselli, Caterina Pisani, Luca Pratelli
Corresponding AuthorsCaterina Pisani
Emailscaterina.pisani@unisi.it

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