A computationally fast alternative to cross-validation in penalized Gaussian graphical models
A computationally fast alternative to cross-validation in penalized Gaussian graphical models
复制标题
惩罚高斯图模型中交叉验证的计算快速替代方案
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
E. Wit
中科院分区：
文献类型：
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作者：
I. Vujačić;A. Abbruzzo;E. Wit
我们研究了惩罚高斯图模型中选择正则化参数的问题。当目标是获得具有良好预测能力的模型时，交叉验证是黄金标准。我们提出了高斯图形模型中 Kullback-Leibler 损失的新估计器，它提供了交叉验证的计算快速替代方案。估计量是通过近似留一交叉验证获得的。我们的方法在各种类型的图表的模拟数据集上进行了演示。与其他可用的交叉验证替代方案（例如 Akaike 信息准则和广义近似交叉验证）相比，所提出的公式表现出优越的性能，特别是在典型的小样本量场景中。我们还表明，当样本量较小时，估计器可用于提高贝叶斯信息准则的性能。
We study the problem of selecting a regularization parameter in penalized Gaussian graphical models. When the goal is to obtain a model with good predictive power, cross-validation is the gold standard. We present a new estimator of Kullback–Leibler loss in Gaussian Graphical models which provides a computationally fast alternative to cross-validation. The estimator is obtained by approximating leave-one-out-cross-validation. Our approach is demonstrated on simulated data sets for various types of graphs. The proposed formula exhibits superior performance, especially in the typical small sample size scenario, compared to other available alternatives to cross-validation, such as Akaike's information criterion and Generalized approximate cross-validation. We also show that the estimator can be used to improve the performance of the Bayesian information criterion when the sample size is small.
影响因子：
2.1
作者：
Li, HZ;Gui, J
通讯作者：
Gui, J