Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309910 
Year of Publication: 
2023
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 64 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 1095-1117
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Subdata
D-optimality
Massive data
Polynomial regression
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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