TY - JOUR

T1 - Modeling heavy-tailed bounded data by the trapezoidal beta distribution with applications

AU - Figueroa-Zu'Niga , Jorge I.

AU - Niklitschek-Soto, Sebastian

AU - Leiva, Víctor

AU - Liu, Shuangzhe

PY - 2020

Y1 - 2020

N2 - In this paper, by using a new method, we derive the trapezoidal beta (TB) distribution and its properties. The TB distribution is a mixture model, generalizes both the beta and rectangular beta distributions, and allows one to describe bounded data with heavy right and/or left tails. In relation to the two-parameter beta distribution, we add two additional parameters which have an intuitive interpretation. The four TB parameters are estimated with the expectation-maximization algorithm. We conduct a simulation study to evaluate performance of the TB distribution. An application with real data is carried out, which includes a comparison among the beta, rectangular beta and TB distributions indicating that the TB one describes these data better.

AB - In this paper, by using a new method, we derive the trapezoidal beta (TB) distribution and its properties. The TB distribution is a mixture model, generalizes both the beta and rectangular beta distributions, and allows one to describe bounded data with heavy right and/or left tails. In relation to the two-parameter beta distribution, we add two additional parameters which have an intuitive interpretation. The four TB parameters are estimated with the expectation-maximization algorithm. We conduct a simulation study to evaluate performance of the TB distribution. An application with real data is carried out, which includes a comparison among the beta, rectangular beta and TB distributions indicating that the TB one describes these data better.

M3 - Article

SP - 1

EP - 19

JO - Revstat Statistical Journal

JF - Revstat Statistical Journal

SN - 1645-6726

ER -