An α-Monotone Generalized Log-Moyal Distribution with Applications to Environmental Data - Talha Arslan (Mathematics, 2021)
Modeling environmental data plays a crucial role in explaining environmental phenomena. In some cases, well-known distributions, e.g., Weibull, inverse Weibull, and Gumbel distributions, cannot model environmental events adequately. Therefore, many authors tried to find new statistical distributions to represent environmental phenomena more accurately. In this paper, an <semantics>α</semantics>-monotone generalized log-Moyal (<semantics>α</semantics>-GlogM) distribution is introduced and some statistical properties such as cumulative distribution function, hazard rate function (hrf), scale-mixture representation, and moments are derived. The hrf of the <semantics>α</semantics>-GlogM distribution can form a variety of shapes including the bathtub shape. The <semantics>α</semantics>-GlogM distribution converges to generalized half-normal (GHN) and inverse GHN distributions. It reduces to slash GHN and <semantics>α</semantics>-monotone inverse GHN distributions for certain parameter settings. Environmental data sets are used to show implementations of the <semantics>α</semantics>-GlogM distribution and also to compare its modeling performance with its rivals. The comparisons are carried out using well-known information criteria and goodness-of-fit statistics. The comparison results show that the <semantics>α</semantics>-GlogM distribution is preferable over its rivals in terms of the modeling capability.