A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control
نویسندگان
چکیده
منابع مشابه
A Fuzzy Permutation Method for False Discovery Rate Control
Biomedical researchers often encounter the large-p-small-n situations-a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample size studies. The method introduces fuzziness into standard permutation analysis to produce randomized p-values, which are then converted into q-val...
متن کاملFeature selection in “omics” prediction problems using cat scores and false non-discovery rate control
We revisit the problem of feature selection in linear discriminant analysis (LDA), i.e. when features are correlated. First, we introduce a pooled centroids formulation of the multi-class LDA predictor function, in which the relative weights of Mahalanobis-tranformed predictors are given by correlation-adjusted t scores (cat scores). Second, for feature selection we propose thresholding cat sco...
متن کاملFalse Discovery Rate Control With Groups.
In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when dealing with multiplicity problems to increase statistical power. In this article, we demonstrat...
متن کاملPrivate False Discovery Rate Control
We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certain conditions. Our general approach is to adapt a well-known variant of the Benjamini-Hochberg procedure (BHq), making each step differentially private. This destroys the classical proof of FDR control. To prove FDR co...
متن کاملOptimal weighting for false discovery rate control
How to weigh the Benjamini-Hochberg procedure? In the context of multiple hypothesis testing, we propose a new step-wise procedure that controls the false discovery rate (FDR) and we prove it to be more powerful than any weighted Benjamini-Hochberg procedure. Both finitesample and asymptotic results are presented. Moreover, we illustrate good performance of our procedure in simulations and a ge...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE/ACM Transactions on Computational Biology and Bioinformatics
سال: 2018
ISSN: 1545-5963,1557-9964,2374-0043
DOI: 10.1109/tcbb.2017.2780106