# WLreg: A new re-parametrization of the Weighted Lindley distribution and its regression model

## TL;DR

This paper introduces a new regression model based on a re-parametrized Lindley distribution for analyzing skewed positive data, showing it outperforms existing models.

## Contribution

The novel WL2 regression model and its software implementation for handling skewed positive dependent variables.

## Key findings

- The WL2 model outperforms gamma, extended gamma, and Maxwell-Boltzmann-exponential regression models.
- Maximum likelihood estimation proves efficient for the new model's parameters.
- The model is effectively applied to a real-world house price dataset.

## Abstract

A novel re-parametrization of the weighted Lindley distribution is introduced to develop a regression model suitable for skewed dependent variables defined on ℝ+. This new model is called the WL2 regression model. It is shown to outperform existing models such as the gamma, extended gamma, and Maxwell-Boltzmann-exponential regression models. Parameter estimation is performed using the maximum likelihood estimation technique, and the efficiency of these estimates is assessed through a simulation study. An application to a house price data set is presented to highlight the importance of the WL2 regression model. In addition, we propose the WLreg software, accessible via https://bartinuni.shinyapps.io/WLreg, to facilitate the application of the new regression model for practitioners in the field.