Abstract
In this study, we propose an estimation method for normal mean problem that can have unknown sparsity as well as correlations in the signals. Our proposed method first decomposes arbitrary dependent covariance matrix of the observed signals into two parts: common dependence and weakly dependent error terms. By subtracting common dependence, the correlations among the signals are significantly weakened. It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. Using simulated examples that have moderate to high degrees of sparsity and different dependent structures in the signals, we demonstrate that the performance of our proposed algorithm is favorable compared to the existing method which assumes the signals are independent identically distributed. Furthermore, our approach is applied on the widely used “Hapmap” gene expressions data, and our results are consistent with the findings in other studies.
Publisher
Public Library of Science (PLoS)
Reference22 articles.
1. Calibration and empirical Bayes variable selection;E. George;Biometrika,2000
2. Diagnosis of multiple cancer types by shrunken centroids of gene expression;R. Tibshirani;Proceedings Of The National Academy Of Sciences,2002
3. An introduction to variable and feature selection;I. Guyon;Journal Of Machine Learning Research,2003
4. Raykar, V. & Zhao, L. Nonparametric prior for adaptive sparsity. Proceedings Of The Thirteenth International Conference On Artificial Intelligence And Statistics. pp. 629–636 (2010)
5. Adapting to unknown sparsity by controlling the false discovery rate;F. Abramovich;The Annals Of Statistics,2006
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献