PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
arXiv preprint arXiv:1506.03850 , year=
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abstract
We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, linear, or a low-complexity curve, as determined by the data. We present a blockwise coordinate descent procedure for efficiently optimizing the penalized likelihood objective over a dense grid of the tuning parameter, producing a regularization path of additive models. We demonstrate the performance of our method on both real and simulated data examples, and compare it with existing techniques for additive model selection.
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UNVERDICTED 2representative citing papers
Review of variable selection and functional form methods in multivariable analysis finds insufficient evidence for recommendations and highlights seven topics needing further study.
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PRADAS: PRior-Assisted DAta Splitting for False Discovery Rate Control
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
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State-of-the-art in selection of variables and functional forms in multivariable analysis -- outstanding issues
Review of variable selection and functional form methods in multivariable analysis finds insufficient evidence for recommendations and highlights seven topics needing further study.