all AI news
Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
May 2, 2024, 4:45 a.m. | Zhaoxing Gao
stat.ML updates on arXiv.org arxiv.org
Abstract: Ridge regression is an indispensable tool in big data econometrics but suffers from bias issues affecting both statistical efficiency and scalability. We introduce an iterative strategy to correct the bias effectively when the dimension $p$ is less than the sample size $n$. For $p>n$, our method optimally reduces the bias to a level unachievable through linear transformations of the response. We employ a Ridge-Screening (RS) method to handle the remaining bias when $p>n$, creating a …
abstract arxiv bias big big data data econ.em econometrics efficiency form inference iterative regression ridge sample scalability solution statistical stat.me stat.ml strategy tool type
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US