1 code implementation • 30 Jun 2021 • Patrick Chao, William Fithian
We propose a new empirical Bayes method for covariate-assisted multiple testing with false discovery rate (FDR) control, where we model the local false discovery rate for each hypothesis as a function of both its covariates and p-value.
1 code implementation • 20 Mar 2019 • Kenneth Hung, William Fithian
Large-scale replication studies like the Reproducibility Project: Psychology (RP:P) provide invaluable systematic data on scientific replicability, but most analyses and interpretations of the data fail to agree on the definition of "replicability" and disentangle the inexorable consequences of known selection bias from competing explanations.
Methodology Applications 62F03, 62P25
1 code implementation • 21 Mar 2016 • Stefan Wager, William Fithian, Percy Liang
The framework imagines data as being drawn from a slice of a Levy process.
no code implementations • 8 Dec 2015 • William Fithian, Jonathan Taylor, Robert Tibshirani, Ryan Tibshirani
Extending the selected-model tests of Fithian et al. (2014), we construct p-values for each step in the path which account for the adaptive selection of the model path using the data.
no code implementations • NeurIPS 2014 • Stefan Wager, William Fithian, Sida Wang, Percy Liang
Dropout training, originally designed for deep neural networks, has been successful on high-dimensional single-layer natural language tasks.
no code implementations • 20 Aug 2013 • William Fithian, Rahul Mazumder
We propose a general framework for reduced-rank modeling of matrix-valued data.
no code implementations • 16 Jun 2013 • William Fithian, Trevor Hastie
By contrast, our estimator is consistent for $\theta^*$ provided that the pilot estimate is.