On a family of discrete log-symmetric distributions

Abstract

The use of continuous probability distributions has been widespread in problems with purely discrete nature. In general, such distributions are not appropriate in this scenario. In this paper, we introduce a class of discrete and asymmetric distributions based on the family of continuous log-symmetric distributions. Some properties are discussed as well as estimation by the maximum likelihood method. A Monte Carlo simulation study is carried out to evaluate the performance of the estimators, and censored and uncensored data sets are used to illustrate the proposed methodology.

Density Sharpening: Principles and Applications to Discrete Data Analysis

“…It is interesting to contrast our finding with Saulo et al (2020), where the authors fit a highly specialized nonparametric discrete distribution to this data. The beauty of our approach is that it performs nonparametric correction (through d) only when it is warranted.…”