Identification of influential observations in high-dimensional cancer survival data through the rank product test-Reference-Cited by-同舟云学术

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Identification of influential observations in high-dimensional cancer survival data through the rank product test

Published:2018-02-14 Issue:1 Volume:11 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Carrasquinha Eunice^ORCID,Veríssimo André,Lopes Marta B.,Vinga Susana

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Genetics,Molecular Biology,Biochemistry

Link

http://link.springer.com/content/pdf/10.1186/s13040-018-0162-z.pdf

Reference26 articles.

1. Cox DR. Regression models and life-tables. J R Stat Soc Ser B (Methodol). 1972; 34(2):187–220.

2. Kalbeisch JD, Prentice RL. The Statistical Analysis of Failure Time Data, 2nd edn. Hoboken: Wiley; 2002.

3. Bednarski T. Robust estimation in cox’s regression model. Scand J Stat. 1993; 20(3):213–25.

4. Heritier S, Cantoni E, Copt S, Victoria-Feser MP. Robust Methods in Biostatistics. New York: Wiley; 2009.

5. Nardi A, Schemper M. New residuals for cox regression and their application to outlier screening. Biometrics. 1999; 55(2):523–9.

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