Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects

Kaymaz, Ö, Alqahtani, K, Wood, HM orcid.org/0000-0003-3009-5904 et al. (1 more author) (2020) Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects. Journal of Applied Statistics. ISSN 0266-4763

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Copyright, Publisher and Additional Information: © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of a paper published in Journal of Applied Statistics. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Tumour, lung cancer, pathological subtype, logistic regression, sparse solution, hierarchical likelihood
Dates:
  • Accepted: 23 February 2020
  • Published (online): 11 March 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 31 Mar 2020 19:12
Last Modified: 01 Apr 2020 10:12
Status: Published online
Publisher: Taylor & Francis
Identification Number: https://doi.org/10.1080/02664763.2020.1738358

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