scholarly journals An Empirical Bayes Approach to Robust Variance Estimation: A Statistical Proposal for Quantitative Medical Image Testing

2012 ◽  
Vol 02 (03) ◽  
pp. 260-268
Author(s):  
Zhan-Qian John Lu ◽  
Charles Fenimore ◽  
Ronald H Gottlieb ◽  
Carl C. Jaffe
Keyword(s):  
Medical Image ◽  
Empirical Bayes ◽  
Variance Estimation ◽  
Robust Variance ◽  
Robust Variance Estimation ◽  
Empirical Bayes Approach ◽  
Bayes Approach

scholarly journals An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

2021 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

Listening Ears or Reading Eyes: A Meta-Analysis of Reading and Listening Comprehension Comparisons

2021 ◽  
pp. 003465432110608
Author(s):  
Virginia Clinton-Lisell
Keyword(s):  
Listening Comprehension ◽  
Variance Estimation ◽  
Meta Analysis ◽  
Age Groups ◽  
Robust Variance ◽  
Reading Condition ◽  
P 16 ◽  
Robust Variance Estimation ◽  
Literal Comprehension

In this study, a meta-analysis of reading and listening comprehension comparisons across age groups was conducted. Based on robust variance estimation (46 studies; N = 4,687), the overall difference between reading and listening comprehension was not reliably different (g = 0.07, p = .23). Reading was beneficial over listening when the reading condition was self-paced (g = 0.13, p = .049) rather than experimenter-paced (g = −0.32, p = .16). Reading also had a benefit when inferential and general comprehension rather than literal comprehension was assessed (g = 0.36, p = .02; g = .15, p = .05; g = −0.01, p = .93, respectively). There was some indication that reading and listening were more similar in languages with transparent orthographies than opaque orthographies (g = 0.001, p = .99; g = 0.10, p = .19, respectively). The findings may be used to inform theories of comprehension about modality influences in that both lower-level skill and affordances vary comparisons of reading and listening comprehension. Moreover, the findings may guide choices of modality; however, both audio and written options are needed for accessible instruction.

An empirical bayes approach to monitoring water quality

1995 ◽  
Vol 6 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Andrew R. Solow ◽  
Arthur G. Gaines
Keyword(s):  
Water Quality ◽  
Empirical Bayes ◽  
Empirical Bayes Approach ◽  
Bayes Approach
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