# Model selection for high-dimensional linear regression with dependent observations

arXiv: https://arxiv.org/abs/1906.07395 · HTML: https://mlsanity.com/paper/1906.07395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python](https://mlsanity.com/paper/2503.16753/) (2025)
- [Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data](https://mlsanity.com/paper/2307.03410/) (2023)
- [High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality](https://mlsanity.com/paper/2505.06531/) (2025)

## Abstract (arXiv)

We investigate the prediction capability of the orthogonal greedy algorithm (OGA) in high-dimensional regression models with dependent observations. The rates of convergence of the prediction error of OGA are obtained under a variety of sparsity conditions. To prevent OGA from overfitting, we introduce a high-dimensional Akaike's information criterion (HDAIC) to determine the number of OGA iterations. A key contribution of this work is to show that OGA, used in conjunction with HDAIC, can achieve the optimal convergence rate without knowledge of how sparse the underlying high-dimensional model is.