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Academic literature on the topic 'Bayes False discovery rate'

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Published: 9 March 2023
Last updated: 11 March 2023

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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 (March 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 (June 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. Options include Efron’s Empirical Bayes FDR, Benjamini-Hochberg FDR control for multiple testing, Lindsey’s method for smoothing empirical distributions, estimation of the mixing proportion, and central matching. We illustrate the important difference between estimating the FDR for a particular finding and adjusting a hypothesis test to control the false discovery propensity. RESULTS/ANTICIPATED RESULTS: We performed a comparison of the capabilities of our new p.fdr function to the popular p.adjust function from the base stats-package. Specifically, we examined multiple examples of data coming from different unknown mixture distributions to highlight the null estimation methods p.fdr includes. The base package does not provide the optimal FDR usage nor sufficient estimation options. We also compared the step-up/step-down procedure used in adjusted p-value hypothesis test and discuss when this is inappropriate. The p.adjust function is not able to report raw-adjusted values and this will be shown in the graphical results. DISCUSSION/SIGNIFICANCE OF IMPACT: FDRs reveal the propensity for an observed result to be incorrect. FDRs should accompany observed results to help contextualize the relevance and potential impact of research findings. Our results show that previous methods are not sufficient rich or precise in their calculations. Our new package allows the user to be in control of the null estimation and step-up implementation when reporting FDRs.
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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 (March 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 (June 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, false discovery rate (FDR)-controlling procedures have been developed that yield more power both by relaxing the stringency of the significance threshold and by retaining more power for a given significance threshold. However, these procedures have been shown to perform poorly for mapping QTLs, principally because they ignore recombination fractions between markers. Here, I describe a procedure that accounts for recombination fractions and extends FDR control to include simultaneous control of the false non-discovery rate, i.e. the overall error rate is controlled. This procedure is developed in the Bayesian framework using a direct posterior probability approach. Data-driven significance thresholds are determined by minimizing the expected loss. The procedure is equivalent to jointly maximizing positive and negative predictive values. In the context of mapping QTLs for experimental crosses, the procedure is applicable to mapping main effects, gene–gene interactions and gene–environment interactions.
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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 (August 18, 2017): e1005700. http://dx.doi.org/10.1371/journal.pcbi.1005700.

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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 hierarchical mixture model using the “smoothing-by-roughening" approach. Under the semiparametric hierarchical mixture model, (i) the prior distribution can be modeled flexibly, (ii) the ODP test statistic and the posterior distribution are analytically tractable, and (iii) computations are easy to implement. In addition, we provide a significance rule based on the false discovery rate (FDR) in the empirical Bayes framework. Applications to two clinical studies are presented.
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Quatto, Piero, Nicolò Margaritella, Isa Costantini, Francesca Baglio, Massimo Garegnani, Raffaello Nemni, and Luigi Pugnetti. "Brain networks construction using Bayes FDR and average power function." Statistical Methods in Medical Research 29, no. 3 (May 14, 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 present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data.
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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 (October 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 and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.
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Tabash, Mohammed, Mohamed Abd Allah, and Bella Tawfik. "Intrusion Detection Model Using Naive Bayes and Deep Learning Technique." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 215–24. http://dx.doi.org/10.34028/iajit/17/2/9.

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The increase of security threats and hacking the computer networks are one of the most dangerous issues should treat in these days. Intrusion Detection Systems (IDSs), are the most appropriate methods to prevent and detect the attacks of networks and computer systems. This study presents several techniques to discover network anomalies using data mining tasks, Machine learning technology and dependence of artificial intelligence techniques. In this research, the smart hybrid model was developed to explore any penetrations inside the network. The model divides into two basic stages. The first stage includes the Genetic Algorithm (GA) in selecting the characteristics with depends on a process of extracting, Discretize And dimensionality reduction through Proportional K-Interval Discretization (PKID) and Fisher Linear Discriminant Analysis (FLDA) on respectively. At the end of the first stage combining Naïve Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided into two separate groups for training and testing. The second stage completely depends on the first stage outputs (predicted class) and reclassified with multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm Stochastic Gradient Descent (SGD). In order to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms. The comparison of the proposed model and conventional models show the superiority of the proposed model and the previous conventional hybrid models. The result of the proposed model is 99.9325 of classification accuracy, the rate of detection is 99.9738 and 0.00093 of false alarms
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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 correlations. In this regard, the Thesis has dealt with the problem of multiple testing in a Bayesian perspective, by examining in depth the “Bayesian False Discovery Rate” (FDR), already defined in Efron, and by introducing the “Bayesian Power” (BP). The behavior of the FDR and BP estimators has been analyzed both with asymptotic theory and with Monte Carlo simulations; furthermore, it has been investigated the robustness of the proposed estimators by simulating specific pattern of dependencies among the p-values associated to the multiple comparisons. Such a multiple testing approach, that allows to control both FDR and BP, has been applyied to a dataset provided by the Milan Center for Neuroscience (NeuroMi). Once selected a sample of 70 participants, classified properly into young subjects and elderly subjects, subject by subject network models have been constructed in order to verify two alternative theories about changes in the pattern of functional connectivity as time goes by, namely the de-differentiation hypothesis versus the localization hypothesis. This objective has been achieved by selecting some proper network measures in order to verify the original hypotheses about the pattern of functional connectivity in the elderly’s group and in the group of young subjects, and by constructing some ad-hoc measures.
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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, and instead of placing a distribution at the hyper prior level in the hierarchical Bayes model, empirical Bayes estimates the prior parameters using the data via, often, the marginal distribution. In this research, we developed new Bayesian methods under unknown prior distribution. A set of adequate prior distributions maybe defined using Bayesian model checking by setting a threshold on the posterior predictive p-value, prior predictive p-value, calibrated p-value, Bayes factor, or integrated likelihood. We derive a set of adequate posterior distributions from that set. In order to obtain a single posterior distribution instead of a set of adequate posterior distributions, we used a blended distribution, which minimizes the relative entropy of a set of adequate prior (or posterior) distributions to a "benchmark" prior (or posterior) distribution. We present two approaches to generate a blended posterior distribution, namely, updating-before-blending and blending-before-updating. The blended posterior distribution can be used to estimate the LFDR by considering the nonlocal false discovery rate as a benchmark and the different LFDR estimators as an adequate set. The likelihood ratio can often be misleading in multiple testing, unless it is supplemented by adjusted p-values or posterior probabilities based on sufficiently strong prior distributions. In case of unknown prior distributions, they can be estimated by empirical Bayes methods or blended distributions. We propose a general framework for applying the laws of likelihood to problems involving multiple hypotheses by bringing together multiple statistical models. We have applied the proposed framework to data sets from genomics, COVID-19 and other data.
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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
Ph.D.
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 approaches and propose new, improved versions of some FDR controlling procedures available in the literature. A number of adaptive procedures have been put forward in the literature, each attempting to improve the method of Benjamini and Hochberg (1995), the BH method, by incorporating into this method an estimate of number true null hypotheses. Among these, the method of Benjamini, Krieger and Yekutieli (2006), the BKY method, has been receiving lots of attention recently. In this proposal, a variant of the BKY method is proposed by considering a different estimate of number true null hypotheses, which often outperforms the BKY method in terms of the FDR control and power. Storey's (2002) estimation based approach to controlling the FDR has been developed from a class of conservatively biased point estimates of the FDR under a mixture model for the underlying p-values and a fixed rejection threshold for each null hypothesis. An alternative class of point estimates of the FDR with uniformly smaller conservative bias is proposed under the same setup. Numerical evidence is provided to show that the mean squared error (MSE) is also often smaller for this new class of estimates. Compared to Storey's (2002), the present class provides a more powerful estimation based approach to controlling the FDR.
Temple University--Theses
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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 with the outcome. To address the task of performing inference on penalized regression models, this thesis proposes false discovery rate approaches for a broad class of penalized regression models. This work includes the development of an upper bound for the number of noise variables in a model, as well as local false discovery rate approaches that quantify the likelihood of each individual selection being a false discovery. These methods are applicable to a wide range of penalties, such as the lasso, elastic net, SCAD, and MCP; a wide range of models, including linear regression, generalized linear models, and Cox proportional hazards models; and are also extended to the group regression setting under the group lasso penalty. In addition to studying these methods using numerous simulation studies, the practical utility of these methods is demonstrated using real data from several high-dimensional genome wide association studies.
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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 using parametric formulas for the distribution of modularity in random networks, coupled with a false discovery rate (FDR) controller to parcellate rsfMRI data. The proposed method controls the FDR to reduce the number of false positives and incorporates spatial information to ensure the regions are spatially contiguous. Simulations demonstrate that our proposed FDRcontrolled agglomerative clustering algorithm yields more accurate results when compared with existing methods. We applied our proposed method to a rs-fMRI dataset and found that it obtained higher reproducibility compared to the Ward hierarchical clustering method. Lastly, we compared the normalized total connectivity degree of each region within the motor network between normal subjects and Parkinson’s disease (PD) subjects using sub-regions defined by our proposed method and the entire region. We found that PD subjects without medication had a significant increase in functional connectivity compared to normal subjects in the right primary motor cortex using our sub-regions within the right primary motor cortex, whereas this significant increase was not found using the entire right primary motor cortex. These sub-regions are of great interest in studying the differences in functional connectivity between different neurological diseases, which can be used as biomarkers and may provide insight in severity of the disease.
Applied Science, Faculty of
Graduate
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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 research compares the FDR method to Dunnett's test using agronomic data from a study with 196 varieties of dry beans. Simulated data is used to assess type I error and power of the tests. In general, the FDR method provides a higher power than Dunnett's test while maintaining control of the type I error rate.
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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 the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.
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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) for levering GWAS results using summary statistics from a related disease. I extended an existing method to enable a shared control design, increasing power and applicability, and developed an approximate bound on false-discovery rate (FDR) for the procedure. Using the new method I identified several new variant-disease associations. I then developed a second application of shared control design in the context of study replication, enabling improvement in power at the cost of changing the spectrum of sensitivity to systematic errors in study cohorts. This has application in studies on rare diseases or in between-case analyses. I then developed a method for partially characterising heterogeneity within a disease by modelling the bivariate distribution of case-control and within-case effect sizes. Using an adaptation of a likelihood-ratio test, this allows an assessment to be made of whether disease heterogeneity corresponds to differences in disease pathology. I applied this method to a range of simulated and real datasets, enabling insight into the cause of heterogeneity in autoantibody positivity in type 1 diabetes (T1D). Finally, I investigated the relation of subtypes of juvenile idiopathic arthritis (JIA) to adult diseases, using modified genetic risk scores and linear discriminants in a penalised regression framework. The contribution of this thesis is in a range of methodological developments in the analysis of high-dimensional association study comparison. Methods such as these will have wide application in the analysis of GWAS and similar areas, particularly in the development of stratified medicine.
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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. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99888-2.

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

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

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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, 151–60. Beachwood, Ohio, USA: 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, 1391–92. Cham: 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, 1–2. Cham: 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, 504–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_248.

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Rouam, Sigrid. "False Discovery Rate (FDR)." In Encyclopedia of Systems Biology, 731–32. New York, NY: 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, 119–28. New York, NY: 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, 194–201. Cham: 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, 317–23. New York, NY: 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, 383–96. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_36.

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MacDonald, Peter W., Nathan Wilson, Kun Liang, and Yingli Qin. "Controlling the False Discovery Rate of Grouped Hypotheses." In Emerging Topics in Statistics and Biostatistics, 161–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72437-5_8.

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Conference papers on the topic "Bayes False discovery rate"

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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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Napoli, A., and I. Obeid. "Learning Cultured Neuronal Network Evolution Using False Discovery Rate Analysis." In 2013 39th Annual Northeast Bioengineering Conference (NEBEC). IEEE, 2013. http://dx.doi.org/10.1109/nebec.2013.22.

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Li, Junning, Z. Jane Wang, and Martin J. McKeown. "Controlling the false discovery rate in modeling brain functional connectivity." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518057.

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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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