On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies
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DOI: 10.1080/02664760802638116
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- Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- A. Antoniadis, 1997. "Wavelets in statistics: A review," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 97-130, August.
- Jianqing Fan, 1997. "Comments on «Wavelets in statistics: A review» by A. Antoniadis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 131-138, August.
- Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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Keywords
model selection; generalized linear model; non-concave penalized likelihood; high-dimensional data set; deviance; trauma;All these keywords.
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