# False Discovery Rate Control via Debiased Lasso

@article{Javanmard2018FalseDR, title={False Discovery Rate Control via Debiased Lasso}, author={Adel Javanmard and Hamid Javadi}, journal={ArXiv}, year={2018}, volume={abs/1803.04464} }

We consider the problem of variable selection in high-dimensional statistical models where the goal is to report a set of variables, out of many predictors $X_1, \dotsc, X_p$, that are relevant to a response of interest. For linear high-dimensional model, where the number of parameters exceeds the number of samples $(p>n)$, we propose a procedure for variables selection and prove that it controls the \emph{directional} false discovery rate (FDR) below a pre-assigned significance level $q\in [0…

## 42 Citations

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It is shown that the proposed debiased statistics can asymptotically control the directional (sign) FDR and directional false discovery variables at a pre-specified significance level for two-sample problems.

### A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models

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### False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation

- Computer ScienceJournal of the American Statistical Association
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The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data--driven threshold along the ranking to control the FDR, and establishes the asymptotic validity of SDA for both the FDR and false discovery proportion (FDP) control under mild regularity conditions.

### Stepdown SLOPE for Controlled Feature Selection

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- 2023

Two new SLOPEs are proposed to realize control of high-dimensional feature selection by adaptively imposing the non-increasing sequence of tuning parameters on the sorted L-One Penalized Estimation by considering the stepdown-based SLOPE to control the probability of false rejections and false discovery proportion.

### Inference in Sparsity-Induced Weak Factor Models

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- MathematicsJournal of the American Statistical Association
- 2021

Global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings and a lower bound for the global testing is established, which shows that the proposed test is asymptotically minimax optimal over some sparsity range.

### Inference in Weak Factor Models

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### Power of FDR Control Methods: The Impact of Ranking Algorithm, Tampered Design, and Symmetric Statistic

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The Rare/Weak signal model is adopted, popular in multiple testing and variable selection literature, and the rate of convergence of the number of false positives and the numberof false negatives of FDR control methods for particular classes of designs is characterized.

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