A novel algorithm based on optimal approximate design theory selects nearly optimal subdata for parametric model parameter estimation, with a convergence proof and tight efficiency bounds that outperform prior methods.
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Nearly Optimal Subdata Selection
A novel algorithm based on optimal approximate design theory selects nearly optimal subdata for parametric model parameter estimation, with a convergence proof and tight efficiency bounds that outperform prior methods.