Optimal subsampling design for polynomial regression in one covariate

Torsten Reuter () and Rainer Schwabe ()
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Torsten Reuter: Otto von Guericke University Magdeburg
Rainer Schwabe: Otto von Guericke University Magdeburg

Statistical Papers, 2023, vol. 64, issue 4, No 6, 1095-1117

Abstract: Abstract Improvements in technology lead to increasing availability of large data sets which makes the need for data reduction and informative subsamples ever more important. In this paper we construct D-optimal subsampling designs for polynomial regression in one covariate for invariant distributions of the covariate. We study quadratic regression more closely for specific distributions. In particular we make statements on the shape of the resulting optimal subsampling designs and the effect of the subsample size on the design. To illustrate the advantage of the optimal subsampling designs we examine the efficiency of uniform random subsampling.

Keywords: Subdata; D-optimality; Massive data; Polynomial regression; Primary: 62K05; Secondary: 62R07 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00362-023-01425-0

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