Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions-Reference-Cited by-同舟云学术

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Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions

Published:2015-05-26 Issue:23 Volume:34 Page:3133-3143
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statist. Med.

Author:

Greenland Sander¹²,Mansournia Mohammad Ali³

Affiliation:

1. Department of Epidemiology, Fielding School of Public Health; University of California; Los Angeles CA U.S.A.

2. Department of Statistics, College of Letters and Science; University of California; Los Angeles CA U.S.A.

3. Department of Epidemiology and Biostatistics, School of Public Health; Tehran University of Medical Sciences; Tehran Iran

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Reference41 articles.

1. Bias reduction of maximum likelihood estimates;Firth;Biometrika,1993

2. A solution to the problem of monotone likelihood in Cox regression;Heinze;Biometrics,2001

3. Fixing the nonconvergence bug in logistic regression with SPLUS and SAS;Heinze;Computer Methods and Programs in Biomedicine,2003

4. Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets;Heinze;Statistics in Medicine,2010

5. SAS and SPLUS programs to perform Cox without convergence problems;Heinze;Computer Methods and Programs in Biomedicine,2002

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