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双峰威布尔分布:性质与推断

A bimodal Weibull distribution: properties and inference.

作者信息

Vila Roberto, Niyazi Çankaya Mehmet

机构信息

Departamento de Estatística, Universidade de Brasília, Brasília, Brazil.

Faculty of Applied Sciences, Department of International Trading and Finance, Uşak University, Uşak, Turkey.

出版信息

J Appl Stat. 2021 May 24;49(12):3044-3062. doi: 10.1080/02664763.2021.1931822. eCollection 2022.

Abstract

Modelling is challenging topic and using parametric models is important stage to reach flexible function for modelling. Weibull distribution has shape and scale parameters which play the main role for modelling. Bimodality parameter is added and so bimodal Weibull distribution can capture real data set with bimodality which can be actually combination of two populations. The properties of the proposed distribution and estimation method are examined extensively to show its usability in modelling accurately and safely for practitioners. After examination as first stage in modelling issue, it is appropriate to use bimodal Weibull for modelling bimodality in real data sets if it exists. Two estimation methods including objective functions are used to estimate the parameters of shape, scale and bimodality parameters of function. The second stage in modelling is overcome by using heuristic algorithms for optimization of function according to parameters due to the fact that converging to global point of objective function is performed by heuristic algorithms from stochastic optimization. Real data sets are provided to show the modelling competence of objective functions from bimodal forms of Weibull and Gamma distributions having well defined shape, scale and bimodality parameters and potentially less parameters when compared with the existing distributions.

摘要

建模是一个具有挑战性的主题,使用参数模型是实现灵活建模功能的重要阶段。威布尔分布具有形状和尺度参数,它们在建模中起着主要作用。添加了双峰性参数,因此双峰威布尔分布可以捕捉具有双峰性的实际数据集,而这实际上可能是两个总体的组合。对所提出的分布和估计方法的性质进行了广泛研究,以向从业者展示其在准确和安全建模方面的可用性。在作为建模问题的第一阶段进行检验之后,如果实际数据集中存在双峰性,则使用双峰威布尔进行建模是合适的。使用包括目标函数在内的两种估计方法来估计函数的形状、尺度和双峰性参数。建模的第二阶段通过使用启发式算法根据参数对函数进行优化来克服,因为从随机优化的启发式算法可以实现收敛到目标函数的全局点。提供了实际数据集,以展示具有明确形状、尺度和双峰性参数且与现有分布相比潜在参数更少的威布尔和伽马分布双峰形式的目标函数的建模能力。

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引用本文的文献

1
A general class of trimodal distributions: properties and inference.一类一般的三峰分布:性质与推断。
J Appl Stat. 2023 May 8;51(8):1446-1469. doi: 10.1080/02664763.2023.2207785. eCollection 2024.
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A model for bimodal rates and proportions.双峰率与比例模型。
J Appl Stat. 2022 Nov 18;51(4):664-681. doi: 10.1080/02664763.2022.2146661. eCollection 2024.

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