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• W2293772259 abstract "Subsampling strategies are derived to sample a small portion of design (data) points in a low-dimensional linear regression model $y=Xbeta+varepsilon$ with near-optimal statistical rates. Our results apply to both problems of estimation of the underlying linear model $beta$ and predicting the real-valued response $y$ of a new data point $x$. The derived subsampling strategies are minimax optimal under the fixed design setting, up to a small $(1+epsilon)$ relative factor. We also give interpretable subsampling probabilities for the random design setting and demonstrate explicit gaps in statistial rates between optimal and baseline (e.g., uniform) subsampling methods." @default.
• W2293772259 created "2016-06-24" @default.
• W2293772259 creator A5066109863 @default.
• W2293772259 creator A5077874981 @default.
• W2293772259 date "2016-01-09" @default.
• W2293772259 modified "2023-09-27" @default.
• W2293772259 title "Minimax Subsampling for Estimation and Prediction in Low-Dimensional Linear Regression." @default.
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• W2293772259 hasPublicationYear "2016" @default.
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