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WLreg：加权林德利分布及其回归模型的一种新的重新参数化。

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

作者信息

Altun Emrah, Chesneau Christophe, Alqifari Hana N

机构信息

Department of Mathematics, Bartin University, Bartin, Turkey.

Department of Mathematics, University of Caen-Normandie, Caen, France.

出版信息

PLoS One. 2025 Jun 9;20(6):e0324005. doi: 10.1371/journal.pone.0324005. eCollection 2025.

DOI:10.1371/journal.pone.0324005
PMID:40489444
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12148117/
Abstract

A novel re-parametrization of the weighted Lindley distribution is introduced to develop a regression model suitable for skewed dependent variables defined on [Formula: see text]. 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.

摘要

引入了加权林德利分布的一种新的重新参数化方法，以开发一种适用于定义在[公式：见文本]上的偏态因变量的回归模型。这个新模型被称为WL2回归模型。结果表明，它优于现有的模型，如伽马、扩展伽马和麦克斯韦 - 玻尔兹曼 - 指数回归模型。使用最大似然估计技术进行参数估计，并通过模拟研究评估这些估计的效率。给出了一个房价数据集的应用，以突出WL2回归模型的重要性。此外，我们提出了WLreg软件，可通过https://bartinuni.shinyapps.io/WLreg访问，以方便该领域的从业者应用新的回归模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3824/12148117/9e2f3b3c7a65/pone.0324005.g013.jpg
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