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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Exact Tail Asymptotics of Dirichlet Distributions
[Submitted on 1 Apr 2009 (v1), last revised 29 Jul 2026 (this ve · 2009-04-01 · via math.ST updates on arXiv.org

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Abstract:Let $\X=A^\top R\U$ be a linearly transformed generalised symmetrised Dirichlet scale mixture in $\R^k$, $k\ge2$. For a fixed direction $\b\in(0,\infty)^k$, we derive an exact asymptotic expansion of $\pk{\X>\vk t_n}$ for eventually positive threshold vectors $\vk t_n$ described relative to the quadratic-programming minimiser on the natural active and residual Gumbel scales; residual limits equal to $-\infty$ are allowed. The radial distribution is assumed to belong to the Gumbel max-domain of attraction. The local power and constant are determined by the local product-power behaviour of the angular density near the minimising direction. The result includes the ray $\vk t_n=u_n\b$ and yields an explicit comparison with the associated elliptical model, a conditional weak limit for the locally rescaled vector and the limiting location of the smallest component under a high common threshold. The minimum overshoot is asymptotically exponential and independent of its location. The finite-dimensional Gaussian minimum and location limits are recovered as a special case.

Submission history

From: Enkelejd Hashorva [view email]
[v1] Wed, 1 Apr 2009 12:50:26 UTC (17 KB)
[v2] Mon, 19 Apr 2010 09:58:14 UTC (17 KB)
[v3] Wed, 29 Jul 2026 20:05:45 UTC (26 KB)