Principles of experimental design for big data analysis
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Publication:1750251
DOI10.1214/16-STS604zbMath1442.62174WikidataQ41615597 ScholiaQ41615597MaRDI QIDQ1750251
James M. McGree, Christopher C. Holmes, Elizabeth G. Ryan, Sylvia Richardson, Christopher C. Drovandi, Kerrie L. Mengersen
Publication date: 18 May 2018
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ss/1504253123
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Robust active learning with binary responses ⋮ Bivariate Residual Plots With Simulation Polygons ⋮ Design of experiments and machine learning to improve robustness of predictive maintenance with application to a real case study ⋮ Information-based optimal subdata selection for non-linear models ⋮ Accounting for outliers in optimal subsampling methods ⋮ A model robust subsampling approach for generalised linear models in big data settings ⋮ Predictive Subdata Selection for Computer Models ⋮ Subdata selection based on orthogonal array for big data ⋮ Generation of all randomizations using circuits ⋮ Experimental Design Issues in Big Data: The Question of Bias ⋮ On greedy heuristics for computing D-efficient saturated subsets
Uses Software
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