We develop penalized empirical likelihood for parameter estimation and variable selection in high-dimensional generalized linear models. By using adaptive lasso penalty function, we show that the proposed estimator has the oracle property. Also, we consider the problem of testing hypothesis, and show that the nonparametric profiled empirical likelihood ratio statistic has asymptotic chi-square distribution. Some simulations and an application are given to illustrate the performance of the proposed method.

中文翻译：

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高维广义线性模型的惩罚经验似然

我们针对高维广义线性模型中的参数估计和变量选择开发了惩罚性经验似然法。通过使用自适应套索惩罚函数，我们证明了所提出的估计器具有oracle属性。此外，我们考虑了检验假设的问题，并表明非参数剖析经验似然比统计量具有渐近卡方分布。给出了一些仿真和应用实例，说明了该方法的性能。