ArticleBiostatistics (Oxford, England)2013
Controlling the local false discovery rate in the adaptive Lasso.
Article in Biostatistics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis.Scientific reports · 2024Article
- Feature-specific inference for penalized regression using local false discovery rates.Statistics in medicine · 2023Article
- The Noise Collector for sparse recovery in high dimensions.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies.Bioinformatics (Oxford, England) · 2019Article
- Widespread changes in mRNA stability contribute to quiescence-specific gene expression patterns in a fibroblast model of quiescence.BMC genomics · 2017Article
- Article
- Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits.Genetics · 2015Article
- Structured variable selection with q-values.Biostatistics (Oxford, England) · 2013Article
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4 authors.
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Abstract
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn-δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.
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