Relevant bibliographies by topics / Bayes False discovery rate

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Academic literature on the topic 'Bayes False discovery rate'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Bayes False discovery rate"

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Sarkar, Sanat K., and Tianhui Zhou. "Controlling Bayes directional false discovery rate in random effects model." Journal of Statistical Planning and Inference 138, no. 3 (2008): 682–93. http://dx.doi.org/10.1016/j.jspi.2007.01.006.

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Hollister, Megan C., and Jeffrey D. Blume. "4497 Accessible False Discovery Rate Computation." Journal of Clinical and Translational Science 4, s1 (2020): 44. http://dx.doi.org/10.1017/cts.2020.164.

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OBJECTIVES/GOALS: To improve the implementation of FDRs in translation research. Current statistical packages are hard to use and fail to adequately convey strong assumptions. We developed a software package that allows the user to decide on assumptions and choose the hey desire. We encourage wider reporting of FDRs for observed findings. METHODS/STUDY POPULATION: We developed a user-friendly R function for computing FDRs from observed p-values. A variety of methods for FDR estimation and for FDR control are included so the user can select the approach most appropriate for their setting. Optio
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Muralidharan, Omkar. "An empirical Bayes mixture method for effect size and false discovery rate estimation." Annals of Applied Statistics 4, no. 1 (2010): 422–38. http://dx.doi.org/10.1214/09-aoas276.

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SHRINER, DANIEL. "Mapping multiple quantitative trait loci under Bayes error control." Genetics Research 91, no. 3 (2009): 147–59. http://dx.doi.org/10.1017/s001667230900010x.

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SummaryIn mapping of quantitative trait loci (QTLs), performing hypothesis tests of linkage to a phenotype of interest across an entire genome involves multiple comparisons. Furthermore, linkage among loci induces correlation among tests. Under many multiple comparison frameworks, these problems are exacerbated when mapping multiple QTLs. Traditionally, significance thresholds have been subjectively set to control the probability of detecting at least one false positive outcome, although such thresholds are known to result in excessively low power to detect true positive outcomes. Recently, fa
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Noma, Hisashi, and Shigeyuki Matsui. "An Empirical Bayes Optimal Discovery Procedure Based on Semiparametric Hierarchical Mixture Models." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/568480.

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Multiple testing has been widely adopted for genome-wide studies such as microarray experiments. For effective gene selection in these genome-wide studies, the optimal discovery procedure (ODP), which maximizes the number of expected true positives for each fixed number of expected false positives, was developed as a multiple testing extension of the most powerful test for a single hypothesis by Storey (Journal of the Royal Statistical Society, Series B,vol. 69, no. 3, pp. 347–368, 2007). In this paper, we develop an empirical Bayes method for implementing the ODP based on a semiparametric hie
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Quatto, Piero, Nicolò Margaritella, Isa Costantini, et al. "Brain networks construction using Bayes FDR and average power function." Statistical Methods in Medical Research 29, no. 3 (2019): 866–78. http://dx.doi.org/10.1177/0962280219844288.

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Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the pres
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Amar, David, Ron Shamir, and Daniel Yekutieli. "Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate." PLOS Computational Biology 13, no. 8 (2017): e1005700. http://dx.doi.org/10.1371/journal.pcbi.1005700.

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Yang, Zhenyu, Zuojing Li, and David R. Bickel. "Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate." BMC Bioinformatics 14, no. 1 (2013): 87. http://dx.doi.org/10.1186/1471-2105-14-87.

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You, Na, and Xueqin Wang. "An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data." Journal of Bioinformatics and Computational Biology 15, no. 05 (2017): 1750020. http://dx.doi.org/10.1142/s0219720017500202.

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The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters a
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Hossin, Md Murad, F. M. Javed Mehedi Shamrat, Md Rifat Bhuiyan, Rabea Akter Hira, Tamim Khan, and Shourav Molla. "Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2446–56. http://dx.doi.org/10.11591/beei.v12i4.4448.

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According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is be
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Dissertations / Theses on the topic "Bayes False discovery rate"

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DI, BRISCO AGNESE MARIA. "Statistical Network Analysis: a Multiple Testing Approach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/96090.

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The problem of identifying connections between nodes in a network model is of fundamental importance in the analysis of brain networks because each node represents a specific brain region that can potentially be connected to other brain regions by means of functional relations; the dynamical behavior of each node can be quantified by adopting a correlation measure among time series. In this contest, the whole set of links between nodes in a network can be represented by means of an adjacency matrix with high dimension, that can be obtained by performing a huge number of simultaneous tests on
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Rahal, Abbas. "Bayesian Methods Under Unknown Prior Distributions with Applications to The Analysis of Gene Expression Data." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42408.

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The local false discovery rate (LFDR) is one of many existing statistical methods that analyze multiple hypothesis testing. As a Bayesian quantity, the LFDR is based on the prior probability of the null hypothesis and a mixture distribution of null and non-null hypothesis. In practice, the LFDR is unknown and needs to be estimated. The empirical Bayes approach can be used to estimate that mixture distribution. Empirical Bayes does not require complete information about the prior and hyper prior distributions as in hierarchical Bayes. When we do not have enough information at the prior level
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Liu, Fang. "New Results on the False Discovery Rate." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/96718.

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Statistics<br>Ph.D.<br>The false discovery rate (FDR) introduced by Benjamini and Hochberg (1995) is perhaps the most standard error controlling measure being used in a wide variety of applications involving multiple hypothesis testing. There are two approaches to control the FDR - the fixed error rate approach of Benjamini and Hochberg (BH, 1995) where a rejection region is determined with the FDR below a fixed level and the estimation based approach of Storey (2002) where the FDR is estimated for a fixed rejection region before it is controlled. In this proposal, we concentrate on both these
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Miller, Ryan. "Marginal false discovery rate approaches to inference on penalized regression models." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6474.

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Data containing large number of variables is becoming increasingly more common and sparsity inducing penalized regression methods, such the lasso, have become a popular analysis tool for these datasets due to their ability to naturally perform variable selection. However, quantifying the importance of the variables selected by these models is a difficult task. These difficulties are compounded by the tendency for the most predictive models, for example those which were chosen using procedures like cross-validation, to include substantial amounts of noise variables with no real relationship wit
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Wong, Adrian Kwok-Hang. "False discovery rate controller for functional brain parcellation using resting-state fMRI." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58332.

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Parcellation of brain imaging data is desired for proper neurological interpretation in resting-state functional magnetic resonance imaging (rs-fMRI) data. Some methods require specifying a number of parcels and using model selection to determine the number of parcels with rs-fMRI data. However, this generalization does not fit with all subjects in a given dataset. A method has been proposed using parametric formulas for the distribution of modularity in random networks to determine the statistical significance between parcels. In this thesis, we propose an agglomerative clustering algorithm u
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Kubat, Jamie. "Comparing Dunnett's Test with the False Discovery Rate Method: A Simulation Study." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/27025.

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Recently, the idea of multiple comparisons has been criticized because of its lack of power in datasets with a large number of treatments. Many family-wise error corrections are far too restrictive when large quantities of comparisons are being made. At the other extreme, a test like the least significant difference does not control the family-wise error rate, and therefore is not restrictive enough to identify true differences. A solution lies in multiple testing. The false discovery rate (FDR) uses a simple algorithm and can be applied to datasets with many treatments. The current resea
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Guo, Ruijuan. "Sample comparisons using microarrays -- application of false discovery rate and quadratic logistic regression." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-010808-173747/.

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Guo, Ruijuan. "Sample comparisons using microarrays: - Application of False Discovery Rate and quadratic logistic regression." Digital WPI, 2008. https://digitalcommons.wpi.edu/etd-theses/28.

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In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of
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Dalmasso, Cyril. "Estimation du positive False Discovery Rate dans le cadre d'études comparatives en génomique." Paris 11, 2006. http://www.theses.fr/2006PA11T015.

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Liley, Albert James. "Statistical co-analysis of high-dimensional association studies." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270628.

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Modern medical practice and science involve complex phenotypic definitions. Understanding patterns of association across this range of phenotypes requires co-analysis of high-dimensional association studies in order to characterise shared and distinct elements. In this thesis I address several problems in this area, with a general linking aim of making more efficient use of available data. The main application of these methods is in the analysis of genome-wide association studies (GWAS) and similar studies. Firstly, I developed methodology for a Bayesian conditional false discovery rate (cFDR)
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Books on the topic "Bayes False discovery rate"

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Desai, Tejas A. Important Applications of the Behrens-Fisher Statistic and the False Discovery Rate. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99888-2.

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Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2023.

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Bickel, David R. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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Bickel, David R. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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Bickel, David. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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Galwey, Nicholas W. False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Wiley & Sons, Incorporated, John, 2022.

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Galwey, Nicholas W. False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Wiley & Sons, Incorporated, John, 2024.

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Desai, Tejas A. Important Applications of the Behrens-Fisher Statistic and the False Discovery Rate. Springer International Publishing AG, 2022.

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Book chapters on the topic "Bayes False discovery rate"

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Tang, Weihua, and Cun-Hui Zhang. "Empirical Bayes methods for controlling the false discovery rate with dependent data." In Complex Datasets and Inverse Problems. Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000111.

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Cobia, Derin. "False Discovery Rate." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9057.

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Cobia, Derin. "False Discovery Rate." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_9057-2.

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Storey, John D. "False Discovery Rate." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_248.

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Storey, John D. "False Discovery Rate." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_229.

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Rouam, Sigrid. "False Discovery Rate (FDR)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_223.

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Aggarwal, Suruchi, and Amit Kumar Yadav. "False Discovery Rate Estimation in Proteomics." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3106-4_7.

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Carroll, Hyrum D., Alex C. Williams, Anthony G. Davis, and John L. Spouge. "False Discovery Rate for Homology Searches." In Advances in Bioinformatics and Computational Biology. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02624-4_18.

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Bhattacharya, Rabi, Lizhen Lin, and Victor Patrangenaru. "Multiple Testing and the False Discovery Rate." In Springer Texts in Statistics. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4032-5_13.

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Clements, Nicolle, Sanat K. Sarkar, and Wenge Guo. "Astronomical Transient Detection Controlling the False Discovery Rate." In Lecture Notes in Statistics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_36.

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Conference papers on the topic "Bayes False discovery rate"

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Koka, Taulant, Jasin Machkour, and Michael Muma. "False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715414.

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Jeong, Minoh, Martina Cardone, and Alex Dytso. "Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619564.

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Qiu, Shizheng, Xuehui Zhang, Yue Liu, Zhishuai Zhang, Yang Hu, and Yadong Wang. "Gene ontology and conjunctional false discovery rate statistical framework revealed shared genetic mechanisms underlying glaucoma and myopia." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822498.

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Bei, Yuanzhe, and Pengyu Hong. "Significance analysis by minimizing false discovery rate." In 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2012. http://dx.doi.org/10.1109/bibm.2012.6392652.

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Xiang, Yu. "Distributed False Discovery Rate Control with Quantization." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849383.

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McHugh, J. Mike, Janusz Konrad, Venkatesh Saligrama, Pierre-Marc Jodoin, and David Castanon. "Motion detection with false discovery rate control." In 2008 15th IEEE International Conference on Image Processing - ICIP 2008. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4711894.

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Wong, Adrian, Martin J. McKeown, Mehdi Moradi, and Z. Jane Wang. "False discovery rate controller for functional brain parcellation." In 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2016. http://dx.doi.org/10.1109/ccece.2016.7726782.

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Krylov, Vladimir A., Gabriele Moser, Sebastiano B. Serpico, and Josiane Zerubia. "False discovery rate approach to image change detection." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738787.

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Chen, Jie, Wenyi Zhang, and H. Vincent Poor. "On parallel sequential change detection controlling false discovery rate." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869004.

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pour, Ali Foroughi, and Lori A. Dalton. "Optimal Bayesian Feature Selection with Bounded False Discovery Rate." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645491.

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Reports on the topic "Bayes False discovery rate"

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Clendinen, Chaevien, Javier Flores, Lisa Bramer, David Degnan, Vanessa Paurus, and Yuri Eberlim de Corilo. Enter Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1985304.

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Neuert, Mark, and Smitha Koduru. PR-244-173856-R01 In-line Inspection Crack Tool Reliability and Performance Evaluation. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011599.

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The ability for operators to make operational and maintenance decisions based on in-line inspection (ILI) data depends on the performance of ILI tools with respect to sizing and detection of crack and stress corrosion cracking (SCC) features. A series of previous Pipeline Research Council International, Inc. (PRCI) projects created a database of ILI tool and pipe excavation data that can be used to evaluate the detection and sizing capabilities of ultrasonic (UT) (NDE-4-E Phase 1, PR-244-133731) and electromagnetic acoustic (EMAT) (NDE-4-E Phase 2, PR-244-153719) ILI technologies. This current
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Boyle, M. Terrestrial vegetation monitoring at Chattahoochee River National Recreation Area: 2021 data summary. National Park Service, 2024. http://dx.doi.org/10.36967/2303257.

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The Southeast Coast Network (SECN) conducts long-term terrestrial vegetation monitoring as part of the NPS Inventory and Monitoring Program. The vegetation community vital sign is one of the primary-tier resources identi?ed by SECN park managers, and monitoring is conducted at 15 network parks (DeVivo et al. 2008). Monitoring plants and their associated communities over time allows for targeted understanding of ecosystems within the SECN geography, which provides managers information about the degree of change within their parks? natural vegetation. 2021 marked the ?rst year of conducting this
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