# Directional FDR Control for Sub-Gaussian Sparse GLMs

@inproceedings{Cui2021DirectionalFC, title={Directional FDR Control for Sub-Gaussian Sparse GLMs}, author={Chang Cui and Jinzhu Jia and Yijun Xiao and Huiming Zhang}, year={2021} }

High-dimensional sparse generalized linear models (GLMs) have emerged in the setting that the number of samples and the dimension of variables are large, and even the dimension of variables grows faster than the number of samples. False discovery rate (FDR) control aims to identify some small number of statistically significantly nonzero results after getting the sparse penalized estimation of GLMs. Using the CLIME method for precision matrix estimations, we construct the debiased-Lasso…

## 3 Citations

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A decorrelated score test based on the decor related score function is proposed and proved and the asymptotic normality of the score function without the inﬂuence of many nuisance parameters under the assumption that accelerates the convergence of the MCMC method is proved.

### Inference and FDR Control for Simulated Ising Models in High-dimension

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Under mild conditions that ensure a specific convergence rate of MCMC method, the l1 consistency of Elastic-net-penalized MCMC-MLE is proved and a decor related score test is proposed based on the decorrelated score function to prove the asymptotic normality of the score function without the influence of many nuisance parameters.

### Heterogeneous Overdispersed Count Data Regressions via Double-Penalized Estimations

- MathematicsMathematics
- 2022

Recently, the high-dimensional negative binomial regression (NBR) for count data has been widely used in many scientific fields. However, most studies assumed the dispersion parameter as a constant,…

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