f-divergence regression models for compositional data

IF 1.1 Q3 STATISTICS & PROBABILITY
A. Alenazi
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引用次数: 0

Abstract

The paper considers the class of $f$-divergence regression models as alternatives to parametric regression models for compositional data. The special cases examined in this paper include the Jensen-Shannon, Kullback-Leibler, Hellinger, chi^2 and total variation divergence. Strong advantages of the proposed regression models are a) the absence of parametric assumptions and b) the ability to treat zero values (which commonly occur in practice) naturally. Extensive Monte Carlo simulation studies comparatively assess the performance of the models in terms of bias and an empirical evaluation using real data examining further aspects, such as predictive performance and computational cost. The results reveal that Kullback-Leibler and Jensen-Shannon divergence regression models exhibited high quality performance in multiple directions. Ultimately, penalised versions of the Kullback-Leibler divergence regression are introduced and illustrated using real data rendering this model the optimal model to utilise in practice.
成分数据的f-散度回归模型
本文考虑了一类$f$-散度回归模型作为成分数据的参数回归模型的替代方案。本文研究的特例包括Jensen Shannon、Kullback-Leibler、Hellinger、chi^2和总变异散度。所提出的回归模型的强大优势是a)没有参数假设,b)能够自然地处理零值(这在实践中常见)。广泛的蒙特卡洛模拟研究比较评估了模型在偏差方面的性能,并使用真实数据进行实证评估,进一步考察了预测性能和计算成本等方面。结果表明,Kullback-Leibler和Jensen-Shannon散度回归模型在多个方向上表现出高质量的性能。最后,引入了Kullback-Leibler散度回归的惩罚版本,并使用实际数据对其进行了说明,使该模型成为实践中使用的最佳模型。
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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来源期刊
CiteScore
3.30
自引率
26.70%
发文量
53
期刊介绍: Pakistan Journal of Statistics and Operation Research. PJSOR is a peer-reviewed journal, published four times a year. PJSOR publishes refereed research articles and studies that describe the latest research and developments in the area of statistics, operation research and actuarial statistics.
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