Multivariate Normal Variance Mixtures in R: The R Package nvmix

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Erik Hintz, M. Hofert, C. Lemieux
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引用次数: 2

Abstract

We present the features and implementation of the R package nvmix for the class of normal variance mixtures including Student t and normal distributions. The package provides functionalities for such distributions, notably the evaluation of the distribution and density function as well as likelihood-based parameter estimation. The distributional family is specified through the quantile function of the underlying mixing random variable. The R package nvmix thus allows one to model multivariate distributions well beyond the classical multivariate normal and t case. Additional functionalities include graphical goodness-of-fit assessment, the estimation of the risk measures value-at-risk and expected shortfall for univariate normal variance mixture distributions and functions to work with normal variance mixture copulas, such as sampling and the evaluation of normal variance mixture copulas and their densities. Furthermore, the package nvmix also provides functionalities for the evaluation of the distribution and density function as well as random variate generation for the more general class of grouped normal variance mixtures.
R中的多元正态方差混合:R包混合
我们介绍了R包nvmix的特征和实现,用于包括Student t和正态分布在内的正态方差混合类。该软件包为这种分布提供了功能,特别是分布和密度函数的评估以及基于似然的参数估计。分布族是通过底层混合随机变量的分位数函数指定的。因此,R包nvmix允许对多变量分布进行建模,远远超出了经典的多变量正态分布和t情况。附加功能包括图形拟合优度评估,单变量正态方差混合分布的风险度量值和预期不足的估计,以及与正态方差混合copuls一起工作的函数,例如采样和正态方差混合copuls及其密度的评估。此外,nvmix包还提供了用于评估分布和密度函数的功能,以及用于更一般类别的分组正态方差混合物的随机变量生成功能。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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