Experimental Design Issues in Big Data: The Question of Bias-Reference-Cited by-同舟云学术

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Experimental Design Issues in Big Data: The Question of Bias

Published:2019 Issue: Volume: Page:193-201
ISSN:1431-8814
Container-title:Statistical Learning of Complex Data
language:
Short-container-title:

Author:

Pesce Elena,Riccomagno Eva,Wynn Henry P.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-21140-0_20

Reference16 articles.

1. Box, G.E., Draper, N.R.: A basis for the selection of a response surface design. J. Am. Stat. Assoc. 54(287), 622–654 (1959)

2. Cheng, C.S., Li, K.C.: A minimax approach to sample surveys. Ann. Stat. 11, 552–563 (1983)

3. Drovandi, C.C., Holmes, C., McGree, J.M., Mengersen, K., Richardson, S., Ryan, E.G.: Principles of experimental design for big data analysis. Stat. Sci. 32(3), 385–404 (2017)

4. Drton, M., Weihs, L.: Generic identifiability of linear structural equation models by ancestor decomposition. Scand. J. Stat. 43(4), 1035–1045 (2016)

5. Grant, W.C., Anstrom, K.J.: Minimizing selection bias in randomized trials: A Nash equilibrium approach to optimal randomization. J. Econ. Behav. Organ. 66(3), 606–624 (2008)

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1. Generation of all randomizations using circuits;Annals of the Institute of Statistical Mathematics;2022-12-23

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