Sperrin, Matthew and Jaki, Thomas (2012) Recovering Independent Associations in Genetics: A Comparison. Journal of Computational Biology, 19 (8). 978–987. ISSN 1066-5277Full text not available from this repository.
In genetics, it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, for addressing this so-called “true sparsity recovery” issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialized methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.
|Journal or Publication Title:||Journal of Computational Biology|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited On:||28 May 2012 11:56|
|Last Modified:||23 Oct 2016 03:14|
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