Regression modeling on stratified data with the lasso

2015/08/22 by Édouard Ollier, Vivian Viallon, Ollier, Edouard +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gene expression and cancer classification #Genetic and phenotypic traits in livestock #Methodology (stat.ME) #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1508.05476

openalex publication_date 2015/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the estimation of regression models on strata defined using a categorical covariate, in order to identify interactions between this categorical covariate and the other predictors. A basic approach requires the choice of a reference stratum. We show that the performance of a penalized version of this approach depends on this arbitrary choice. We propose a refined approach that bypasses this arbitrary choice, at almost no additional computational cost. Regarding model selection consistency, our proposal mimics the strategy based on an optimal and covariate-specific choice for the reference stratum. Results from an empirical study confirm that our proposal generally outperforms the basic approach in the identification and description of the interactions. An illustration on gene expression data is provided.

Citations

• A coordinate-wise optimization algorithm for the Fused Lasso
• A Sparse-Group Lasso
• L1Penalized Estimation in the Cox Proportional Hazards Model
• Least angle regression
• On pattern recovery of the fused Lasso

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• Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules under Effect Heterogeneity
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