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pro vyhledávání: '"Chen, Xiongzhi"'
Autor:
Chen, Xiongzhi
We prove a strong law of large numbers for simultaneously testing parameters of a large number of dependent, Lancaster bivariate random variables with infinite supports, and discuss its implications.
Comment: 11 pages
Comment: 11 pages
Externí odkaz:
http://arxiv.org/abs/2003.02805
Autor:
Zhang, Faith, Chen, Xiongzhi
For a weighted false discovery rate (FDR) procedure for multiple testing the means of equicorrelated normal random variables, we provide an analytic, non-asymptotic, uniform FDR upper bound for its FDR. Two additional and related results are also pro
Externí odkaz:
http://arxiv.org/abs/1910.10868
Autor:
Chen, Xiongzhi, Sarkar, Sanat K.
False discovery rate (FDR) control in structured hypotheses testing is an important topic in simultaneous inference. Most existing methods that aim to utilize group structure among hypotheses either employ the groupwise mixture model or weight all p-
Externí odkaz:
http://arxiv.org/abs/1908.05319
Autor:
Chen, Xiongzhi
We consider estimating the proportion of random variables for two types of composite null hypotheses: (i) the means or medians of the random variables belonging to a non-empty, bounded interval; (ii) the means or medians of the random variables belon
Externí odkaz:
http://arxiv.org/abs/1906.10246
Autor:
Chen, Xiongzhi, Sarkar, Sanat K.
Multiple testing with discrete p-values routinely arises in various scientific endeavors. However, procedures, including the false discovery rate (FDR) controlling Benjamini-Hochberg (BH) procedure, often used in such settings, being developed origin
Externí odkaz:
http://arxiv.org/abs/1906.01701
Autor:
Chen, Xiongzhi
Publikováno v:
Journal of Multivariate Analysis, 2019
The proportion of false null hypotheses is a very important quantity in statistical modelling and inference based on the two-component mixture model and its extensions, and in control and estimation of the false discovery rate and false non-discovery
Externí odkaz:
http://arxiv.org/abs/1807.03889
Autor:
Jeng, X. Jessie, Chen, Xiongzhi
Variable selection methods have been developed in linear regression to provide sparse solutions. Recent studies have focused on further interpretations on the sparse solutions in terms of false positive control. In this paper, we consider false negat
Externí odkaz:
http://arxiv.org/abs/1804.07416
Autor:
Jeng, X. Jessie, Chen, Xiongzhi
We propose a ranking and selection procedure to prioritize relevant predictors and control false discovery proportion (FDP) of variable selection. Our procedure utilizes a new ranking method built upon the de-sparsified Lasso estimator. We show that
Externí odkaz:
http://arxiv.org/abs/1804.03274
Autor:
Chen, Xiongzhi
Publikováno v:
Biometrical Journal, 2020
For multiple testing based on p-values with c\`{a}dl\`{a}g distribution functions, we propose an FDR procedure "BH+" with proven conservativeness. BH+ is at least as powerful as the BH procedure when they are applied to super-uniform p-values. Furthe
Externí odkaz:
http://arxiv.org/abs/1803.06040
Publikováno v:
In Transportation Geotechnics May 2022 34