Relevant bibliographies by topics / False Discovery Proportion control

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'False Discovery Proportion control'

Author: Grafiati
Published: 8 February 2025
Last updated: 31 July 2025

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Journal articles on the topic "False Discovery Proportion control"

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Genovese, Christopher R., and Larry Wasserman. "Exceedance Control of the False Discovery Proportion." Journal of the American Statistical Association 101, no. 476 (2006): 1408–17. http://dx.doi.org/10.1198/016214506000000339.

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Dudziński, Marcin, and Konrad Furmańczyk. "A note on control of the false discovery proportion." Applicationes Mathematicae 36, no. 4 (2009): 397–418. http://dx.doi.org/10.4064/am36-4-2.

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Shang, Shulian, Qianhe Zhou, Mengling Liu, and Yongzhao Shao. "Sample Size Calculation for Controlling False Discovery Proportion." Journal of Probability and Statistics 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/817948.

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The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under
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Goeman, Jelle J., Rosa J. Meijer, Thijmen J. P. Krebs, and Aldo Solari. "Simultaneous control of all false discovery proportions in large-scale multiple hypothesis testing." Biometrika 106, no. 4 (2019): 841–56. http://dx.doi.org/10.1093/biomet/asz041.

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Summary Closed testing procedures are classically used for familywise error rate control, but they can also be used to obtain simultaneous confidence bounds for the false discovery proportion in all subsets of the hypotheses, allowing for inference robust to post hoc selection of subsets. In this paper we investigate the special case of closed testing with Simes local tests. We construct a novel fast and exact shortcut and use it to investigate the power of this approach when the number of hypotheses goes to infinity. We show that if a minimal level of signal is present, the average power to d
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Ge, Yongchao, and Xiaochun Li. "Control of the False Discovery Proportion for Independently Tested Null Hypotheses." Journal of Probability and Statistics 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/320425.

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Consider the multiple testing problem of testingmnull hypothesesH1,…,Hm, among whichm0hypotheses are truly null. Given theP-values for each hypothesis, the question of interest is how to combine theP-values to find out which hypotheses are false nulls and possibly to make a statistical inference onm0. Benjamini and Hochberg proposed a classical procedure that can control the false discovery rate (FDR). The FDR control is a little bit unsatisfactory in that it only concerns the expectation of the false discovery proportion (FDP). The control of the actual random variable FDP has recently drawn
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Jeng, X. Jessie, and Xiongzhi Chen. "Predictor ranking and false discovery proportion control in high-dimensional regression." Journal of Multivariate Analysis 171 (May 2019): 163–75. http://dx.doi.org/10.1016/j.jmva.2018.12.006.

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Zhang, Xiaohua Douglas. "An Effective Method for Controlling False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens." Journal of Biomolecular Screening 15, no. 9 (2010): 1116–22. http://dx.doi.org/10.1177/1087057110381783.

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In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for d
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Zhang, Xiaohua Douglas, Raul Lacson, Ruojing Yang, et al. "The Use of SSMD-Based False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens." Journal of Biomolecular Screening 15, no. 9 (2010): 1123–31. http://dx.doi.org/10.1177/1087057110381919.

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In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statisticall
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Ventura, Valérie, Christopher J. Paciorek, and James S. Risbey. "Controlling the Proportion of Falsely Rejected Hypotheses when Conducting Multiple Tests with Climatological Data." Journal of Climate 17, no. 22 (2004): 4343–56. http://dx.doi.org/10.1175/3199.1.

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Abstract The analysis of climatological data often involves statistical significance testing at many locations. While the field significance approach determines if a field as a whole is significant, a multiple testing procedure determines which particular tests are significant. Many such procedures are available, most of which control, for every test, the probability of detecting significance that does not really exist. The aim of this paper is to introduce the novel “false discovery rate” approach, which controls the false rejections in a more meaningful way. Specifically, it controls a prior
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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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Dissertations / Theses on the topic "False Discovery Proportion control"

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Blain, Alexandre. "Reliable statistical inference : controlling the false discovery proportion in high-dimensional multivariate estimators." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG083.

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La sélection de variables sous contrôle statistique est un problème fondamental rencontré dans divers domaines où les praticiens doivent évaluer l'importance des variables d'entrée par rapport à un résultat d'intérêt. Dans ce contexte, le contrôle statistique vise à limiter la proportion de fausses découvertes, c'est-à-dire la proportion de variables sélectionnées qui sont indépendantes du résultat d'intérêt. Dans cette thèse, nous développons des méthodes visant à assurer un contrôle statistique dans des contextes de grande dimension tout en conservant la puissance statistique. Nous présenton
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Afriyie, Prince. "Applications of Procedures Controlling the Tail Probability of the False Discovery Proportion." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/367548.

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Statistics<br>Ph.D.<br>Multiple testing has been an active area of statistical research in the past decade mainly because of its wide scope of applicability in modern scientific investigations. One major application area of multiple testing is in identifying differentially expressed genes from massive biological data generated by high-throughput genomic technologies where the expression profiles of genes are compared across two or more experimental conditions on a genomic scale. This dissertation briefly reviews modern multiple testing methodologies including types of error control, and multip
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Benditkis, Julia [Verfasser], Arnold [Akademischer Betreuer] Janssen, and Helmut [Akademischer Betreuer] Finner. "Martingale Methods for Control of False Discovery Rate and Expected Number of False Rejections / Julia Benditkis. Gutachter: Arnold Janssen ; Helmut Finner." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2015. http://d-nb.info/1077295170/34.

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"Regaining control of false findings in feature selection, classification, and prediction on neuroimaging and genomics data." Tulane University, 2018.

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acase@tulane.edu<br>The technological advances of past decades have led to the accumulation of large amounts of genomic and neuroimaging data, enabling novel strategies in precision medicine. These largely rely on machine learning algorithms and modern statistical methods for big biological datasets, which are data-driven rather than hypothesis-driven. These methods often lack guarantees on the validity of the research findings. Because it can be a matter of life and death, when computational methods are deployed in clinical practice in medicine, establishing guarantees on the validity of the
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Book chapters on the topic "False Discovery Proportion control"

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Romano, Joseph P., and Azeem M. Shaikh. "On stepdown control of the false discovery proportion." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 2006. http://dx.doi.org/10.1214/074921706000000383.

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Perone-Pacifico, Marco, and Isabella Verdinelli. "False Discovery Control for Scan Clustering." In Scan Statistics. Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4749-0_13.

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Zhao, Bangxin, and Wenqing He. "Simultaneous Control of False Discovery Rate and Sensitivity Using Least Angle Regressions in High-Dimensional Data Analysis." In Advances and Innovations in Statistics and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08329-7_3.

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Genovese, C. R. "False Discovery Rate Control." In Brain Mapping. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-397025-1.00323-7.

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Liu, Fang, and Sanat K. Sarkar. "A New Adaptive Method to Control the False Discovery Rate." In Recent Advances in Biostatistics. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814329804_0001.

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Omairi, Luiza Barranco, Juliana Silva Barbosa, Andre Luiz Ciclini, and Vanessa Cicclini Guerra. "Hepatitis C treatment experience in a dialysis clinic: nephrologist's perspective." In COLLECTION OF INTERNATIONAL TOPICS IN HEALTH SCIENCE- V1. Seven Editora, 2023. http://dx.doi.org/10.56238/colleinternhealthscienv1-059.

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Since its discovery in 1989, hepatitis C has been gaining relevance due to its potential to develop into chronic liver disease. It is known that its prevalence is higher in dialysis patients, increasing in accordance with the time the patient remains on treatment. The diagnosis in this population may be difficult because of the nonspecific clinical picture, which may be confused with symptoms of uremia, besides variable levels of alanine aminotransferase (ALT), added to false-negative serologies for hepatitis C virus (HCV) and low viremia. Several studies seek to identify the causes of transmi
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Cordes, Eugene H. "Take it off! Take it all off! Drugs for weight reduction." In Hallelujah Moments. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190080457.003.0011.

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Obesity is a major and growing threat to good health to most parts of the world. In the United States, Xenical, marketed over the counter as Alli, is the only drug approved by the Food and Drug Administration for long-term use for weight control. There are several others—Qsymia, Contrave, Belviq, Saxenda—that are approved for short-term use. A number of others, approved earlier, have been withdrawn from the market for patient safety reasons, including the popular combination known as phen-fen. The pharmaceutical industry has found the discovery of effective and safe weight control drugs to be
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Targowski, Andrew. "Information Laws." In Information Technology and Societal Development. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-004-2.ch012.

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The purpose of this chapter is to define information laws which control the development of the global and universal civilizations as well as individual autonomous civilizations. Mankind progresses in proportion to its wisdom, which has roots in practice, acquired skills, available data, and information, concepts and knowledge. To be wise, humankind needs to be both informed and knowledgeable, otherwise it will not survive its own failures. Progress in knowledge was painfully slow as long as the spatial memory was transmitted only by oral tradition. With the inventions of writing and books, the
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Ganeri, Jonardon. "The Enigma of Heteronymy." In Virtual Subjects, Fugitive Selves. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198864684.003.0003.

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The two poles around which Pessoa’s entire philosophy of self revolves are commitments to two extremely enigmatic propositions: [simulation] I am a subject other than the subject I am; and [depersonalization] I am merely a forum for the subject I am. What I am calling the ‘enigma of heteronymy’ is the challenge to provide an analysis of the functions of the first person, that is to say, the use or uses of the pronoun ‘I’ and so of the phenomenology of self-consciousness, according to which this pair of propositions is not trivially false but, on the contrary, interestingly and importantly true
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Vinayakumar, R., K. P. Soman, and Prabaharan Poornachandran. "Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch018.

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This article describes how sequential data modeling is a relevant task in Cybersecurity. Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent neural networks (RNNs) are a subset of artificial neural networks (ANNs) which have appeared as a powerful, principle approach to learn dynamic temporal behaviors in an arbitrary length of large-scale sequence data. Furthermore, stacked recurrent neural networks (S-RNNs) have the potential to learn complex temporal behaviors quickly, including sparse representations. To leverage this, the authors model network traf
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Conference papers on the topic "False Discovery Proportion control"

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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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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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Dalleiger, Sebastian, and Jilles Vreeken. "Discovering Significant Patterns under Sequential False Discovery Control." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539398.

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Zhang, B., N. Chenouard, J. C. Olivo-Marin, and V. Meas-Yedid. "Statistical colocalization in biological imaging with false discovery control." In 2008 IEEE International Symposium on Biomedical Imaging: From Macro to Nano (ISBI '08). IEEE, 2008. http://dx.doi.org/10.1109/isbi.2008.4541249.

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Vinzamuri, Bhanukiran, and Kush R. Varshney. "FALSE DISCOVERY RATE CONTROL WITH CONCAVE PENALTIES USING STABILITY SELECTION." In 2018 IEEE Data Science Workshop (DSW). IEEE, 2018. http://dx.doi.org/10.1109/dsw.2018.8439910.

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Nguyen, Hien D., Andrew L. Janke, Nicolas Cherbuin, Geoffrey J. McLachlan, Perminder Sachdev, and Kaarin J. Anstey. "Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies." In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2013. http://dx.doi.org/10.1109/dicta.2013.6691531.

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Halme, Topi, and Visa Koivunen. "Optimal Multi-Stream Quickest Detection with False Discovery Rate Control." In 2023 57th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2023. http://dx.doi.org/10.1109/ieeeconf59524.2023.10476984.

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Flasseur, Olivier, Loic Denis, Eric Thiebaut, and Maud Langlois. "Finding Meaningful Detections: False Discovery Rate Control in Correlated Detection Maps." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287847.

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Wang, Shengze, Shichao Feng, Chongle Pan, and Xuan Guo. "FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995401.

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Reports on the topic "False Discovery Proportion control"

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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.

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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asy
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