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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
Largest Finite Root of Identity-Scale Doubly Singular Bet...
[Submitted on 6 May 2019 (v1), last revised 4 Sep 2026 (this ver · 2019-05-06 · via math.ST updates on arXiv.org

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Abstract:Classical largest-root distributions for Wishart ratios and matrix-variate beta ensembles are usually formulated when the denominator Wishart matrix is nonsingular. In many high-dimensional settings, however, the ambient dimension $p$ exceeds both Wishart degrees of freedom, so the corresponding beta ensemble is doubly singular and the usual matrix product $A^{-1}B$ is not defined. We consider independent central Wishart matrices $A\sim W_p(m,I_p)$ and $B\sim W_p(q,I_p)$ in the regime $p>m\ge q$. For the finite generalized roots of the pair $(B,A)$ or, equivalently, the nonzero eigenvalues of $BA^+$, we prove the exact identity \begin{equation*} \lambda_{\max} \stackrel{d}{=} \lambda_{\max}\left\{W_q(m,I_q)W_q(p-m+q,I_q)^{-1}\right\}. \end{equation*} Here $W_d(r,I_d)$ denotes a $d\times d$ central Wishart matrix with $r$ degrees of freedom and identity scale. Thus, the identity-scale doubly singular beta type II largest-root problem reduces exactly to a nonsingular $q$-dimensional Roy statistic, making its finite-sample CDF directly accessible to classical largest-root algorithms.

Submission history

From: Stepan Grinek [view email]
[v1] Mon, 6 May 2019 00:37:42 UTC (247 KB)
[v2] Tue, 7 May 2019 19:19:28 UTC (247 KB)
[v3] Sat, 4 Jan 2020 17:10:51 UTC (247 KB)
[v4] Fri, 4 Sep 2026 18:59:15 UTC (80 KB)