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CONTROLLING THE FALSE DISCOVERY RATE THROUGH MULTIPLE COMPETITION

Part of: Applications Parametric inference Linear inference, regression Nonparametric inference

Published online by Cambridge University Press:  11 December 2020

KRISTEN EMERY
KRISTEN EMERY*
Affiliation:
School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales2006, Australia
*
e-mail: [email protected]
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Abstract

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Keywords

false discovery rate controlknockoffsmultiple hypotheses testingtarget–decoy competition

MSC classification

Primary: 62F03: Hypothesis testing
Secondary: 62G10: Hypothesis testing 62J05: Linear regression 62P10: Applications to biology and medical sciences
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 104 , Issue 1 , August 2021 , pp. 169 - 170
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Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to the University of Sydney in April 2020; degree approved on 6 August 2020; supervisor Uri Keich.

References

Barber, R. F. and Candès, E. J., ‘Controlling the false discovery rate via knockoffs’, Ann. Statist. 43(5) (2015), 2055–2085.CrossRefGoogle Scholar
Emery, K., Hasam, S., Noble, W. S. and Keich, U., ‘Multiple competition based FDR control’, Preprint, 2019, arXiv:1907.01458.Google Scholar
Emery, K., Hasam, S., Noble, W. S. and Keich, U., ‘Multiple competition-based FDR control and its application to peptide detection’, in: Research in Computational Molecular Biology , Lecture Notes in Computer Science, 12074 (ed. Schwartz, R.) (Springer, Cham, 2020).Google Scholar
Emery, K. and Keich, U., ‘Controlling the FDR in variable selection via multiple knockoffs’, Preprint, 2019, arXiv:1911.09442v2.Google Scholar