Estimating Missing Values Using Neural Networks
研究了用神经网络（反向传播算法）重建缺失值的效果，并与均值法和迭代回归法比较，发现神经网络在训练和测试数据上均表现更优，适合多元分析中的缺失值处理。
The problem of missing values is common in statistical analysis. One approach to deal with missing values is to delete the incomplete cases from the data set. This approach may disregard valuable information, especially in small samples. An alternative approach is to reconstruct the missing values using the information in the data set. The major purpose of this paper is to investigate how a neural network approach performs compared to statistical techniques for reconstructing missing values. The backpropagation algorithm is used as the learning method to reconstruct missing values. The results of back-propagation are compared with results from two methods, viz., (1) using averages, and (2) using iterative regression analysis, to compute missing values. Experimental results show that backpropagation consistently outperforms other methods in both the training and the test data sets, and suggest that the neural network approach is a useful tool for reconstructing missing values in multivariate analysis.