Reliable, timely crop-yield forecasts are essential for market stability and risk management, yet many approaches rely on costly or hard-to-scale inputs. We present a frugal, transferable, and architecture-agnostic deep learning framework that uses routine weather as the only time-varying input plus two lightweight sta...
Fernando Dupin da Cunha Mello, Prashant Kumar, Erick G. Sperandio Nascimento Stricto Sensu Department 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
An agentic framework enhanced with an experience memory designed for the sequential setting and addressing common challenges of sequential decision-making such as credit assignment is introduced, and it is shown that post-game reflection and rule extraction yield measurable improvements on tic-tac-toe without modifying...
Jakub Rada, Viliam Lisý AI Center, Department of Computer Science et al.· 0 citations
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