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摘要翻译：
我们提出了一个新的变分Bayes估计，用于高维Copula的离散或离散与连续相结合的边界。该方法是基于变分逼近的一个可处理的增广后验，并且比以前的基于似然的方法更快。我们用它来估计单变量和多元马尔可夫序数和混合时间序列的可抽蔓Copula。它们具有维数$rt$，其中$t$是观察的数目，$r$是序列的数目，并且很难用以前的方法估计。vine对-Copula是经过仔细选择的，以考虑异方差，这是大多数有序时间序列数据的特征。当与灵活的边距相结合时，所产生的时间序列模型还允许序数数据的其他共同特征，如零膨胀、多模式和不足或过散。通过六个实例序列，我们说明了时间序列copula模型的灵活性，以及变分Bayes估计对多达792维和60个参数的copula的有效性。这远远超过了copula模型对于离散数据的规模和复杂性，而copula模型可以使用以前的方法进行估计。
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英文标题：
《Variational Bayes Estimation of Discrete-Margined Copula Models with
Application to Time Series》
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作者：
Ruben Loaiza-Maya and Michael Stanley Smith
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最新提交年份：
2018
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension $rT$, where $T$ is the number of observations and $r$ is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroskedasticity, which is a feature of most ordinal time series data. When combined with flexible margins, the resulting time series models also allow for other common features of ordinal data, such as zero inflation, multiple modes and under- or over-dispersion. Using six example series, we illustrate both the flexibility of the time series copula models, and the efficacy of the variational Bayes estimator for copulas of up to 792 dimensions and 60 parameters. This far exceeds the size and complexity of copula models for discrete data that can be estimated using previous methods.
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PDF链接：
https://arxiv.org/pdf/1712.09150
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