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首页> 外文期刊>Journal of applied statistics >Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure
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Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure

机译：高维广义线性模型的加权弹性网程序变量选择

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摘要

High-dimensional data arise frequently in modern applications such as biology, chemometrics, economics, neuroscience and other scientific fields. The common features of high-dimensional data are that many of predictors may not be significant, and there exists high correlation among predictors. Generalized linear models, as the generalization of linear models, also suffer from the collinearity problem. In this paper, combining the nonconvex penalty and ridge regression, we propose the weighted elastic-net to deal with the variable selection of generalized linear models on high dimension and give the theoretical properties of the proposed method with a diverging number of parameters. The finite sample behavior of the proposed method is illustrated with simulation studies and a real data example.
机译：高维数据经常出现在现代应用中，例如生物学，化学计量学，经济学，神经科学和其他科学领域。高维数据的共同特征是许多预测变量可能并不重要，并且预测变量之间存在高度相关性。作为线性模型的泛化，广义线性模型也遭受共线性问题。本文结合非凸罚分和岭回归，提出了加权弹性网来处理广义高维线性模型的变量选择，并给出了具有多种参数的方法的理论性质。仿真研究和实际数据示例说明了该方法的有限样本行为。

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