ﻻ يوجد ملخص باللغة العربية
Under weak moment and asymptotic conditions, we offer an affirmative answer to whether the BH procedure (Benjamini and Hochberg, 1995) can control the false discovery rate in testing pairwise comparisons of means under a one-way ANOVA layout. Specifically, despite the fact that the two sample t-statistics do not exhibit positive regression dependency (Benjamini and Yekutieli, 2001), our result shows that the BH procedure can asymptotically control the directional false discovery rate as conjectured by Williams, Jones, and Tukey (1999). Such a result is useful for most general situations when the number of variables is moderately large and/or when idealistic assumptions such as normality and a balanced design are violated.
We are considered with the false discovery rate (FDR) of the linear step-up test $varphi^{LSU}$ considered by Benjamini and Hochberg (1995). It is well known that $varphi^{LSU}$ controls the FDR at level $m_0 q / m$ if the joint distribution of $p$-v
Multiple hypothesis testing, a situation when we wish to consider many hypotheses, is a core problem in statistical inference that arises in almost every scientific field. In this setting, controlling the false discovery rate (FDR), which is the expe
Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false discovery rate (FDR) in multiple hypothesis testing. Inspired by the Ben
We consider the problem of ranking $n$ players from partial pairwise comparison data under the Bradley-Terry-Luce model. For the first time in the literature, the minimax rate of this ranking problem is derived with respect to the Kendalls tau distan
The Bradley-Terry model assigns probabilities for the outcome of paired comparison experiments based on strength parameters associated with the objects being compared. We consider different proposed choices of prior parameter distributions for Bayesi