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Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons
Consider the standard Gaussian linear regression model $Y=X\theta+\epsilon$, where $Y\in R^n$ is a response vector and $ X\in R^{n*p}$ is a design matrix. Numerous... -
A few contributions to variable selection and nonparametric tests
Real-world data are often extremely high-dimensional, severely under constrained and interspersed with a large number of irrelevant or redundant features. Relevant...
