A Class of Skewed Distributions with Applications in Environmental Data

TL;DR

This paper introduces the truncated-logistic skew symmetric (TLSS) distribution, a flexible model for skewed environmental data, with properties, inference methods, and real-world applications demonstrated through simulations and case studies.

Contribution

It proposes a new skewed distribution model (TLSS) based on logistic kernels, with structural properties and inference techniques, tailored for environmental data analysis.

Findings

The TLSS distribution effectively models skewed environmental data.

Maximum likelihood estimation performs well in simulations.

Real data applications demonstrate the model's practical utility.

Abstract

In environmental studies, many data are typically skewed and it is desired to have a flexible statistical model for this kind of data. In this paper, we study a class of skewed distributions by invoking arguments as described by Ferreira and Steel (2006, Journal of the American Statistical Association, 101: 823--829). In particular, we consider using the logistic kernel to derive a class of univariate distribution called the truncated-logistic skew symmetric (TLSS) distribution. We provide some structural properties of the proposed distribution and develop the statistical inference for the TLSS distribution. A simulation study is conducted to investigate the efficacy of the maximum likelihood method. For illustrative purposes, two real data sets from environmental studies are used to exhibit the applicability of such a model.