混合数据类型插补的渐近精确和快速高斯Copula模型
Asymptotically Exact and Fast Gaussian Copula Models for Imputation of Mixed Data Types
摘要：
具有混合数据类型的缺失值是大量机器学习应用中的常见问题，例如调查处理和不同的医疗应用。最近，高斯copula模型已被建议作为一种手段，使用概率框架进行填补缺失值。虽然目前的高斯Copula模型已经显示出最先进的性能，但它们有两个局限性：它们基于快速但可能不精确的近似，并且它们不支持无序的多项式变量。我们使用随机化准蒙特卡罗程序，解决了第一个限制，使用直接和任意精确的近似模型估计和插补。与以前提出的方法相比，我们提供的方法具有较低的估计模型参数和插补值的误差。我们还扩展了以前的高斯copula模型，包括无序多项式变量，除了目前的支持顺序，二进制和连续变量。
英文摘要：
Missing values with mixed data types is a common problem in a large number of machine learning applications such as processing of surveys and in different medical applications. Recently, Gaussian copula models have been suggested as a means of performing imputation of missing values using a probabilistic framework. While the present Gaussian copula models have shown to yield state of the art performance, they have two limitations: they are based on an approximation that is fast but may be imprecise and they do not support unordered multinomial variables. We address the first limitation using direct and arbitrarily precise approximations both for model estimation and imputation by using randomized quasi-Monte Carlo procedures. The method we provide has lower errors for the estimated model parameters and the imputed values, compared to previously proposed methods. We also extend the previous Gaussian copula models to include unordered multinomial variables in addition to the present support of ordinal, binary, and continuous variables.
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