Horseshoe Priors and MDP
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arXiv
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2026
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| author | Polson, Nick Sokolov, Vadim Zantedeschi, Daniel |
| author_facet | Polson, Nick Sokolov, Vadim Zantedeschi, Daniel |
| contents | Carvalho (2010) established two foundational theorems for the horseshoe prior: tight two-sided logarithmic bounds on the marginal density near the origin (Theorem~1.1), and a super-efficient rate of convergence of the Bayes predictive density to the true sampling density in sparse situations (Theorem~2). The ``Shrink Globally, Act Locally'' paper \citep{polson2010shrink} formalised necessary and sufficient conditions on the prior's behaviour at the origin for sparsity adaptation as $p \to \infty$. We show that these results are not merely descriptive properties of the horseshoe -- they are the finite-sample precursors to the asymptotic moderate deviation principle (MDP) of \citet{datta2026newlook}. The log-pole singularity $\piH(θ) \asymp -\log\absθ$ is precisely the origin integrability boundary that selects the MDP threshold $\tcrit = \sqrt{\log(πn/2)}$; super-efficiency below the threshold and tail robustness above it together produce the ABOS Bayes risk $p_0 \log(p/p_0)/n$; and the Clarke--Barron information-theoretic asymptotics of Bayes methods provide the unifying framework in which all three results are faces of a single logarithmic budget principle. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_01266 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Horseshoe Priors and MDP Polson, Nick Sokolov, Vadim Zantedeschi, Daniel Statistics Theory Computation Carvalho (2010) established two foundational theorems for the horseshoe prior: tight two-sided logarithmic bounds on the marginal density near the origin (Theorem~1.1), and a super-efficient rate of convergence of the Bayes predictive density to the true sampling density in sparse situations (Theorem~2). The ``Shrink Globally, Act Locally'' paper \citep{polson2010shrink} formalised necessary and sufficient conditions on the prior's behaviour at the origin for sparsity adaptation as $p \to \infty$. We show that these results are not merely descriptive properties of the horseshoe -- they are the finite-sample precursors to the asymptotic moderate deviation principle (MDP) of \citet{datta2026newlook}. The log-pole singularity $\piH(θ) \asymp -\log\absθ$ is precisely the origin integrability boundary that selects the MDP threshold $\tcrit = \sqrt{\log(πn/2)}$; super-efficiency below the threshold and tail robustness above it together produce the ABOS Bayes risk $p_0 \log(p/p_0)/n$; and the Clarke--Barron information-theoretic asymptotics of Bayes methods provide the unifying framework in which all three results are faces of a single logarithmic budget principle. |
| title | Horseshoe Priors and MDP |
| topic | Statistics Theory Computation |
| url | https://arxiv.org/abs/2604.01266 |