An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-12-27 DOI:10.1007/s40745-022-00460-2
Idika E. Okorie, Emmanuel Afuecheta
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Abstract

In regression analysis involving response variable on the bounded unit interval [0, 1], the beta regression model often suffice as a common choice, however, there are many alternatives to the beta regression model. In this article, we add yet another new alternative to the literature called the unit upper truncated Weibull (unit UTW) regression model. We introduce a novel unit UTW distribution as an alternative to the beta distribution and we present some of its mathematical properties. The unit UTW distribution is then extended to build the unit UTW regression model. Through an extensive Monte-Carlo simulation experiments, we show that the method of maximum likelihood can provide good estimate for each parameter in the new models. We give two practical examples were the proposed models performed better than the beta distribution and the beta regression model.

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β回归模型的替代选择及其在经合组织就业和癌症数据中的应用
在涉及有界单位区间 [0, 1] 上响应变量的回归分析中,贝塔回归模型通常足以作为一种常见的选择,然而,贝塔回归模型有许多替代方案。在这篇文章中,我们为文献增加了另一种新的替代模型,即单位上截断 Weibull(单位 UTW)回归模型。我们介绍了一种新颖的单位 UTW 分布,作为贝塔分布的替代方案,并介绍了它的一些数学特性。然后对单位UTW分布进行扩展,建立单位UTW回归模型。通过大量的蒙特卡罗模拟实验,我们表明最大似然法可以为新模型中的每个参数提供良好的估计值。我们给出了两个实际例子,证明所提出的模型比贝塔分布和贝塔回归模型表现更好。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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