This article aims to provide rigorous and convenient statistical models for dealing with high-variability phenomena. The presence of discrepance in variance represents a substantial issue when it is not possible to reduce variability before analysing the data, leading to the possibility to estimate an inadequate model. In this paper, the application of Generalized Additive Model for Location, Scale and Shape (GAMLSS) and the use of finite mixture model for GAMLSS will be proposed as a solution to the problem of overdispersion. An application to Liver fibrosis data is illustrated in order to identify potential risk factors for patients, which could determine the presence of the disease but also its levels of severity.

中文翻译：

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GAMLSS用于高变异性数据：在肝纤维化病例中的应用

本文旨在为处理高变异性现象提供严格而方便的统计模型。当无法在分析数据之前减小变异性时，方差差异的存在就成为一个实质性问题，从而导致估计模型不充分的可能性。在本文中，将提出应用位置，尺度和形状通用加性模型（GAMLSS）以及将有限混合模型用于GAMLSS的方法，以解决过度分散的问题。说明了在肝纤维化数据中的一种应用，以便确定患者的潜在危险因素，这可以确定疾病的存在以及严重程度。