MAGIC: a general, powerful and tractable method for selective inference

2016/07/09 by Xiaoying Tian, Tian, Xiaoying, Nan Bi +3
Computer Science · Engineering · Mathematics · #Control Systems and Identification #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1607.02630

openalex publication_date 2016/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Selective inference is a recent research topic that tries to perform valid inference after using the data to select a reasonable statistical model. We propose MAGIC, a new method for selective inference that is general, powerful and tractable. MAGIC is a method for selective inference after solving a convex optimization problem with smooth loss and ℓ₁ penalty. Randomization is incorporated into the optimization problem to boost statistical power. Through reparametrization, MAGIC reduces the problem into a sampling problem with simple constraints. MAGIC applies to many ℓ₁ penalized optimization problem including the Lasso, logistic Lasso and neighborhood selection in graphical models, all of which we consider in this paper.

Citations

• Optimal Inference After Model Selection

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• Selective inference with a randomized response

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