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Data Science

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Preprint Jul 2026

Mens: Nonlinear shrinkage estimation in nonparanormal models for financial applications

The MENS attains the robustness of rank-based estimation and the efficiency of nonlinear shrinkage at once within this class, and delivers a better-conditioned covariance estimate, lower realized portfolio volatility, and lower turnover than linear shrinkage in an out-of-sample minimum-variance backtest on S&P 500 stoc...

H. Karamikabir, Mohammad Arashi Department of Statistics, Faculty of Electrical Engineering et al. · 0 citations

Preprint Jul 2026

Restricted nonlinear shrinkage of high-dimensional residual covariance matrices in multivariate regressions

We study estimation of the p*p residual scatter (shape) matrix in a high-dimensional multivariate linear regression, where p and n grow proportionally. When the coefficient matrix obeys a known linear restriction of rank q<d, as in multivariate analysis of variance, growth-curve models, and reduced-rank regression, the...

H. Karamikabir, Mohammad Arashi Department of Statistics, Faculty of Electrical Engineering et al. · 0 citations

#small language model Preprint Aug 2026

uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks

Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (MOFs) remains largely unrealized as large unit cells make first-principles training data expensive to generate, fine-tuned models are scarce...

T. J. Inizan, Prathami Divakar Kamath, Alin M. Elena et al. · 0 citations