Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity

• 成果类型: Article
• 署名作者: Zhao, Junlong; Zhou, Yang; Liu, Yufeng
• 署名单位: Beijing Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2206084
• 发表日期: 2024
• 页码: 1579-1591
• 关键词: Causal Inference; efficient estimation; time-series; BIAS; identification; variables

摘要

High-dimensional linear models are commonly used in practice. In many applications, one is interested in linear transformations beta(T)x of regression coefficients beta epsilon R-p, where x is a specific point and is not required to be identically distributed as the training data. One common approach is the plug-in technique which first estimates beta, then plugs the estimator in the linear transformation for prediction. Despite its popularity, estimation of beta canbe difficult for high-dimensionalproblems. Commonly used assumptions in the literature include that the signal of coefficients beta is sparse and predictors are weakly correlated. These assumptions, however, may not be easily verified, and can be violated in practice. When beta is non-sparse or predictors are strongly correlated, estimation of beta can be very difficult. In this article, we propose a novel point wise estimator for linear transformations of beta. This new estimator greatly relaxes the common assumptions for high-dimensional problems, and is adaptive to the degree of sparsity of beta and strength of correlations among the predictors. In particular, beta can be sparse or nonsparse and predictors can be strongly or weakly correlated. The proposed method is simple for implementation. Numerical and theoretical results demonstrate the competitive advantages of the proposed method for a wide range of problems. Supplementary materials for this article are available online.