Parametric and non-parametric methods to enhance prediction performance in the presence of missing data
Parametric and non-parametric methods to enhance prediction performance in the presence of missing data
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DOI：
10.1109/icstcc.2015.7321316
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Faraj A. A. Bashir-Faraj-A.-A.-Bashir-3106601;Hua-Liang Wei
中科院分区：
文献类型：
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作者：
Faraj A. A. Bashir-Faraj-A.-A.-Bashir-3106601;Hua-Liang Wei
大多数缺失数据分析技术都集中在模型参数估计上，而模型参数估计依赖于现代统计数据分析方法，如极大似然和多重插值。事实上，这些现代方法比传统方法（例如，完整数据分析和均值imputation方法）更好，并且在许多特定应用中可以给出无偏参数估计。由于这些现代方法依赖于线性参数回归，它们不能给出很好的结果，特别是当数据分布具有高度非线性行为时。本文解释了缺失数据情况下的参数估计，包括缺失数据情况下参数估计的概述，并提供了缺失数据情况下非线性参数估计和非参数估计的可访问描述。本文特别关注模型选择方法对缺失数据下的非线性参数和非参数估计的影响。我们还通过一个实例分析来说明这两种方法的性能。
Most missing data analysis techniques have focused on using model parameter estimation which depends on modern statistical data analysis methods such as maximum likelihood and multiple imputation. In fact, these modern methods are better than traditional methods (for example, complete data analysis and mean imputation approaches), and in many particular applications can give unbiased parametric estimation. Because these modern approaches depend on linear parametric regression, they do not give good results, especially if the data distribution has highly nonlinear behaviour. This paper explains parametric estimation in cases of missing data, including an overview of parametric estimation with missing data, and provides accessible descriptions of nonlinear parametric and nonparametric estimation with missing data. In particular, this paper focuses on the effect of model selection methods on nonlinear parametric and nonparametric estimation in the presence of missing data. We also present analysis of an example to illustrate the performance of the two methods.