On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
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Publication:4637075
zbMath1444.62093arXiv1601.02068MaRDI QIDQ4637075
Aarti Singh, Yining Wang, Adams Wei Yu
Publication date: 17 April 2018
Full work available at URL: https://arxiv.org/abs/1601.02068
Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Applications of statistics in engineering and industry; control charts (62P30) Statistical aspects of big data and data science (62R07)
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Optimal Sampling for Generalized Linear Models Under Measurement Constraints ⋮ LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model ⋮ A Local Search Framework for Experimental Design ⋮ Model Checking in Large-Scale Dataset via Structure-Adaptive-Sampling ⋮ Near-optimal discrete optimization for experimental design: a regret minimization approach ⋮ Orthogonal subsampling for big data linear regression ⋮ Unnamed Item ⋮ A branch-and-bound algorithm for the exact optimal experimental design problem ⋮ Gaussian Process Landmarking on Manifolds
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