Variance or Standard Deviation? Shell Geometry and Global-Scale Priors in High-Dimensional Shrinkage

2026-06-22

AI Summary

This study investigates the impact of default prior choices on risk in high-dimensional shrinkage estimation, with a focus on the differing behavior of priors near zero under variance versus standard deviation parameterizations. Through high-dimensional asymptotic analysis, radial power benchmarks, and characterization via the zero-index of scale densities, the work provides the first geometric insight showing that a flat prior on the standard deviation enjoys a unit asymptotic risk advantage near the origin, yielding lower risk under weak signals and second-order equivalence to the flat variance prior under strong signals. The paper further establishes precise risk crossover and equivalence relationships between these two prior classes and offers a unified classification framework for heavy-tailed or sparse priors.

Abstract

We study how the choice of default prior for a common Gaussian scale affects high-dimensional shrinkage risk, highlighting the role played by high-dimensional geometry. Formally, we consider a high-dimensional setting in which the near-zero behavior of the common scale prior has first-order consequences for shrinkage risk, and show that priors that are flat on the variance and those flat on the standard deviation allocate markedly different mass near the zero-scale boundary, leading to distinct shrinkage behavior and informing principled default prior selection. Specifically, under a radial-power benchmark, we establish that the SD-flat benchmark has a one-unit asymptotic risk advantage near the origin, crosses over in the critical regime, and is second-order equivalent to the variance-flat benchmark for strong signals. Proper single global-scale hyperpriors and bounded coordinate-multiplier mixtures inherit these limits through the near-zero exponent of their SD-scale density. For heavier-tailed or sparse priors, that exponent still classifies the common global-scale component, while local-scale tails, model-size priors, or allocation priors can also affect risk.

Problem

• shrinkage risk
• high-dimensional geometry
• global-scale prior
• Gaussian scale
• default prior selection

Innovation

• high-dimensional shrinkage
• global-scale prior
• standard deviation-flat prior
• variance-flat prior
• shrinkage risk