We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true likelihood function. Our approach is built on empirical likelihood, a non-parametric approximation of the likelihood function. We show the asymptotic behaviors of our approach are identical to those seen in empirical likelihood. We demonstrate that our method performs comparably to empirical likelihood while significantly decreasing computational time.

中文翻译：

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分割样本经验似然

我们提出了一种新方法，该方法结合了多个非参数似然类型组件来构建真实似然函数的数据驱动近似。我们的方法建立在经验似然之上，即似然函数的非参数近似。我们展示了我们方法的渐近行为与经验似然中看到的行为相同。我们证明了我们的方法与经验似然相当，同时显着减少了计算时间。