Statistical Methods and Inference
This cluster of papers focuses on regularization and variable selection methods, particularly in the context of high-dimensional data analysis. It covers topics such as Lasso, model selection, sparse models, covariance estimation, survival analysis, random forests, and Bayesian methods.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- Regression Shrinkage and Selection Via the Lasso
- Pattern Recognition and Machine Learning
- Longitudinal data analysis using generalized linear models
- Nearest neighbor pattern classification
- Least angle regression
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Most recent
- Deconfounded-Debiased Distributed Estimation for High-Dimensional Multi-Site Data with Heterogeneous Pervasive Hidden Confounders
- Robust adaptive graph signal estimation using Pseudo–Huber loss
- Maximizing the Out-of-Sample Sharpe Ratio
- Wild Bootstrap for Mean Response Inference in Functional Linear Regression Models
- Robust Design and Misspecification Detection for Count Models
- Efficient Log-Rank Updates for Random Survival Forests