Dual-Dagum分布族:属性、回归及其在COVID-19数据中的应用

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2022-08-26 DOI:10.3233/mas-221354

Elisângela C. Biazatti, G. Cordeiro, Maria do Carmo Soares de Lima

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引用次数: 3

摘要

新的Dual-Dagum-G (da - g)家族被定义为β - g和Kumaraswamy-G发电机的良好竞争对手，这两种发电机广泛应用于几个领域。讨论了它的一些数学性质。得到了最大似然估计，并通过仿真证明了估计的一致性。该家庭的灵活性通过COVID-19数据集得到体现。我们基于DDa-G家族的特殊分布提出了一种新的回归方法，并利用在巴西库里提巴收集的1951例COVID-19患者的数据进行了敏感性分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Dual-Dagum family of distributions: Properties, regression and applications to COVID-19 data

A new Dual-Dagum-G (DDa-G) family is defined as a good competitor to the Beta-G and Kumaraswamy-G generators, which are widely applied in several areas. Some of its mathematical properties are addressed. We obtain the maximum likelihood estimates, and some simulations prove the consistency of the estimates. The flexibility of this family is shown through a COVID-19 data set. We propose a new regression based on a special distribution of the DDa-G family, and provide a sensitivity analysis by using data from 1,951 COVID-19 patients collected in Curitiba, Brazil.

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.