2023/12/25 by Andriëtte Bekker, Bekker, A., Azam Kheyri +3
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2312.15781
openalex publication_date 2023/12/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article explores the estimation of precision matrices in high-dimensional Gaussian graphical models. We address the challenge of improving the accuracy of maximum likelihood-based precision estimation through penalization. Specifically, we consider an elastic net penalty, which incorporates both L1 and Frobenius norm penalties while accounting for the target matrix during estimation. To enhance precision matrix estimation, we propose a novel two-step estimator that combines the strengths of ridge and graphical lasso estimators. Through this approach, we aim to improve overall estimation performance. Our empirical analysis demonstrates the superior efficiency of our proposed method compared to alternative approaches. We validate the effectiveness of our proposal through numerical experiments and application on three real datasets. These examples illustrate the practical applicability and usefulness of our proposed estimator.