A new scope of penalized empirical likelihood with high-dimensional estimating equations
高维估计方程下惩罚经验似然的新框架AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究通用高维经验似然和估计方程，未明确提供结构估计或其他所列方向的应用依据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
高维估计方程下惩罚经验似然的新框架
- 研究问题
- 模型参数与估计方程都处于高维时，如何利用参数稀疏性和估计方程选择，缓解经验似然对维度增长的严格限制？
- 方法与思路
- 作者在经验似然优化中分别惩罚模型参数和拉格朗日乘子，通过鼓励乘子稀疏实现估计方程筛选与有效维度降低。
- 材料陈述的贡献
- 摘要称，在允许两类维度随样本量指数增长的理论框架下，估计量稀疏且一致，非零分量渐近正态，并给出数值与实证示例。
- 阅读关注点
- 应核对指数维度增长的具体限制、两类惩罚及调参条件、估计方程筛选的性质，以及非零分量渐近正态需要哪些条件。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，无法确认稀疏度、矩条件和算法细节；维度增长及渐近结论不能脱离条件使用。
原文摘要
Statistical methods with empirical likelihood (EL) are appealing and effective especially in conjunction with estimating equations for flexibly and adaptively incorporating data information. It is known that EL approaches encounter difficulties when dealing with high-dimensional problems. To overcome the challenges, we begin our study with investigating high-dimensional EL from a new scope targeting at high-dimensional sparse model parameters. We show that the new scope provides an opportunity for relaxing the stringent requirement on the dimensionality of the model parameters. Motivated by the new scope, we then propose a new penalized EL by applying two penalty functions respectively regularizing the model parameters and the associated Lagrange multiplier in the optimizations of EL. By penalizing the Lagrange multiplier to encourage its sparsity, a drastic dimension reduction in the number of estimating equations can be achieved. Most attractively, such a reduction in dimensionality of estimating equations can be viewed as a selection among those high-dimensional estimating equations, resulting in a highly parsimonious and effective device for estimating high-dimensional sparse model parameters. Allowing both the dimensionalities of model parameters and estimating equations growing exponentially with the sample size, our theory demonstrates that our new penalized EL estimator is sparse and consistent with asymptotically normally distributed nonzero components. Numerical simulations and a real data analysis show that the proposed penalized EL works promisingly.