Robust and Sparse Factor Modelling
Robust and Sparse Factor Modelling
复制标题
鲁棒和稀疏因子建模
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Peter Exterkate
中科院分区：
文献类型：
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作者：
C. Croux;Peter Exterkate
因子构建方法被广泛用于通过相对少量的代表性因子来总结大量变量。我们提出了一种新的因素建设过程，享有的属性的鲁棒性离群值和稀疏性，也就是说，有相对较少的非零因素加载。与传统的因子构建方法相比，我们发现，这种方法导致了一个良好的预测性能，在存在离群值和更好的解释因素。我们调查的Monte Carlo实验中的方法的性能，并在一个实证的应用程序从宏观经济学的大数据集。
Factor construction methods are widely used to summarize a large panel of variables by means of a relatively small number of representative factors. We propose a novel factor construction procedure that enjoys the properties of robustness to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to the traditional factor construction method, we find that this procedure leads to a favorable forecasting performance in the presence of outliers and to better interpretable factors. We investigate the performance of the method in a Monte Carlo experiment and in an empirical application to a large data set from macroeconomics.
影响因子：
2.1
作者：
Witten, Daniela M.;Tibshirani, Robert;Hastie, Trevor
通讯作者：
Hastie, Trevor