10 citations · 15 across the 13 of their papers we have counts for
7 papers · 1 filter
Hypergraph Variable Selection with False Discovery Rate Control
Sarah Organ, Toby Kenney, Hong Gu
Variable selection methods that control the false discovery rate often lose power when predictors exhibit complex dependence structures. We previously showed that selecting hierarc…
Setwise Hierarchical Variable Selection and the Generalized Linear Step-Up Procedure for False Discovery Rate Control
Sarah Organ, Toby Kenney, Hong Gu
Controlling the false discovery rate (FDR) in variable selection becomes challenging when predictors are correlated, as existing methods often exclude all members of correlated gro…
Deconvolution density estimation with penalised MLE
Yun Cai, Hong Gu, Toby Kenney
Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all methods in the literature…
SuRF: a New Method for Sparse Variable Selection, with Application in Microbiome Data Analysis
Lihui Liu, Hong Gu, Johan Van Limbergen +1
In this paper, we present a new variable selection method for regression and classification purposes. Our method, called Subsampling Ranking Forward selection (SuRF), is based on L…
Poisson PCA: Poisson Measurement Error corrected PCA, with Application to Microbiome Data
Toby Kenney, Tianshu Huang, Hong Gu
In this paper, we study the problem of computing a Principal Component Analysis of data affected by Poisson noise. We assume samples are drawn from independent Poisson distribution…
Prior Distributions for Ranking Problems
Toby Kenney, Hao He, Hong Gu
The ranking problem is to order a collection of units by some unobserved parameter, based on observations from the associated distribution. This problem arises naturally in a numbe…
The Adequate Bootstrap
Toby Kenney, Hong Gu
There is a fundamental disconnect between what is tested in a model adequacy test, and what we would like to test. The usual approach is to test the null hypothesis "Model M is the…