Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population inference for generalized linear regression when key covariates are completely absent from the target data but observed in a related source population. Standard missing covariat...
Hua-Li Zhao, Kerstin Bunte Bernoulli Institute of Mathematics, Statistics et al.· 0 citations
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
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
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
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