Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
超高维纵向数据的非参数独立筛选与结构识别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：摘要明确针对纵向重复观测，处理个体内相关性并识别常系数与变系数结构，与面板数据方法直接相关。
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…
超高维纵向数据的非参数独立筛选与结构识别
- 研究问题
- 研究超高维纵向数据中，如何筛出相关变量并区分常系数与变系数，同时利用个体内相关性提高后续估计效率。
- 方法与思路
- 作者先用工作独立与 B 样条边际模型筛选，再以组 SCAD 识别系数结构，最后结合协方差估计细化半变系数模型。
- 材料陈述的贡献
- 摘要称，筛选具有高概率保留相关变量的性质，后续步骤具有稀疏性与 oracle 性质，细化估计达到半参数效率。
- 阅读关注点
- 读全文时应核对维度增长、信号强度与相关性条件，以及筛选保证、结构识别和效率结论各自依赖的调参要求。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；所谓较弱条件与计算优势缺少具体比较，有限样本表现不能由摘要独立判断。
原文摘要
Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for finding a sparse semivarying coefficient model, which is widely accepted for longitudinal data analysis. Our proposed method first reduces the number of covariates to a moderate order by employing a screening procedure, and then identifies both the varying and constant coefficients using a group SCAD estimator, which is subsequently refined by accounting for the within-subject correlation. The screening procedure is based on working independence and B-spline marginal models. Under weaker conditions than those in the literature, we show that with high probability only irrelevant variables will be screened out, and the number of selected variables can be bounded by a moderate order. This allows the desirable sparsity and oracle properties of the subsequent structure identification step. Note that existing methods require some kind of iterative screening in order to achieve this, thus they demand heavy computational effort and consistency is not guaranteed. The refined semivarying coefficient model employs profile least squares, local linear smoothing and nonparametric covariance estimation, and is semiparametric efficient. We also suggest ways to implement the proposed methods, and to select the tuning parameters. An extensive simulation study is summarized to demonstrate its finite sample performance and the yeast cell cycle data is analyzed.