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  • 1
    Online Resource
    Online Resource
    Berlin, Heidelberg :Springer Berlin / Heidelberg,
    Keywords: Life sciences -- Mathematical models. ; Electronic books.
    Description / Table of Contents: This monograph offers an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate and related error measures, particularly addressing applications to such fields as genetics, proteomics and neuroscience.
    Type of Medium: Online Resource
    Pages: 1 online resource (182 pages)
    Edition: 1st ed.
    ISBN: 9783642451829
    DDC: 570.15195
    Language: English
    Note: Intro -- Preface -- Contents -- Acronyms -- 1 The Problem of Simultaneous Inference -- 1.1 Sources of Multiplicity -- 1.2 Multiple Hypotheses Testing -- 1.2.1 Measuring and Controlling Errors -- 1.2.2 Structured Systems of Hypotheses -- 1.3 Relationships to Other Simultaneous Statistical Inference Problems -- 1.4 Contributions of this Work -- References -- Part IGeneral Theory -- 2 Some Theory of p-values -- 2.1 Randomized p-values -- 2.1.1 Randomized p-values in Discrete Models -- 2.1.2 Randomized p-values for Testing Composite Null Hypotheses -- 2.2 p-value Models -- 2.2.1 The iid.-Uniform Model -- 2.2.2 Dirac-Uniform Configurations -- 2.2.3 Two-Class Mixture Models -- 2.2.4 Copula Models Under Fixed Margins -- 2.2.5 Further Joint Models -- References -- 3 Classes of Multiple Test Procedures -- 3.1 Margin-Based Multiple Test Procedures -- 3.1.1 Single-Step Procedures -- 3.1.2 Stepwise Rejective Multiple Tests -- 3.1.3 Data-Adaptive Procedures -- 3.2 Multivariate Multiple Test Procedures -- 3.2.1 Resampling-Based Methods -- 3.2.2 Methods Based on Central Limit Theorems -- 3.2.3 Copula-Based Methods -- 3.3 Closed Test Procedures -- References -- 4 Simultaneous Test Procedures -- 4.1 Three Important Families of Multivariate Probability Distributions -- 4.1.1 Multivariate Normal Distributions -- 4.1.2 Multivariate t-distributions -- 4.1.3 Multivariate Chi-Square Distributions -- 4.2 Projection Methods Under Asymptotic Normality -- 4.3 Probability Bounds and Effective Numbers of Tests -- 4.3.1 Sum-Type Probability Bounds -- 4.3.2 Product-Type Probability Bounds -- 4.3.3 Effective Numbers of Tests -- 4.4 Simultaneous Test Procedures in Terms of p-value Copulae -- 4.5 Exploiting the Topological Structure of the Sample Space via Random Field Theory -- References -- 5 Stepwise Rejective Multiple Tests -- 5.1 Some Concepts of Dependency. , 5.2 FWER-Controlling Step-Down Tests -- 5.3 FWER-Controlling Step-Up Tests -- 5.4 FDR-Controlling Step-Up Tests -- 5.5 FDR-Controlling Step-Up-Down Tests -- References -- 6 Multiple Testing and Binary Classification -- 6.1 Binary Classification Under Sparsity -- 6.2 Binary Classification in Non-Sparse Models -- 6.3 Feature Selection for Binary Classification via Higher Criticism -- References -- 7 Multiple Testing and Model Selection -- 7.1 Multiple Testing for Model Selection -- 7.2 Multiple Testing and Information Criteria -- 7.3 Multiple Testing After Model Selection -- 7.3.1 Distributions of Regularized Estimators -- 7.3.2 Two-Stage Procedures -- 7.4 Selective Inference -- References -- 8 Software Solutions for Multiple Hypotheses Testing -- 8.1 The R Package multcomp -- 8.2 The R Package multtest -- 8.3 The R-based μTOSS Software -- 8.3.1 The μTOSS Simulation Tool -- 8.3.2 The μTOSS Graphical User Interface -- References -- Part IIFrom Genotype to Phenotype -- 9 Genetic Association Studies -- 9.1 Statistical Modeling and Test Statistics -- 9.2 Estimation of the Proportion of Informative Loci -- 9.3 Effective Numbers of Tests via Linkage Disequilibrium -- 9.4 Combining Effective Numbers of Tests and Pre-estimation of π0 -- 9.5 Applicability of Margin-Based Methods -- References -- 10 Gene Expression Analyses -- 10.1 Marginal Models and p-values -- 10.2 Dependency Considerations -- 10.3 Real Data Examples -- 10.3.1 Application of Generic Multiple Tests to Large-Scale Data -- 10.3.2 Copula Calibration for a Block of Correlated Genes -- 10.4 LASSO and Statistical Learning Methods -- 10.5 Gene Set Analyses and Group Structures -- References -- 11 Functional Magnetic Resonance Imaging -- 11.1 Spatial Modeling -- 11.2 False Discovery Rate Control for Grouped Hypotheses -- 11.2.1 Clusters of Voxels -- 11.2.2 Multiple Endpoints per Location. , 11.3 Exploiting Topological Structure by Random Field Theory -- 11.4 Spatio-Temporal Models via Multivariate Time Series -- 11.4.1 Which of the Specific Factors have a Non-trivial Autocorrelation Structure? -- 11.4.2 Which of the Common Factors have a Lagged Influence on Which Xi? -- References -- Part IIIFurther Applications in the Life Sciences -- 12 Further Life Science Applications -- 12.1 Brain-Computer Interfacing -- 12.2 Gel Electrophoresis-Based Proteome Analysis -- References -- Index.
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  • 2
    Online Resource
    Online Resource
    Berlin : Weierstraß-Inst. für Angewandte Analysis und Stochastik Leibniz-Inst. im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 37 S., 1.188 KB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1862
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Systemvoraussetzungen: Acrobat reader.
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  • 3
    Keywords: Forschungsbericht ; Immunozyt ; Biomarker ; Epigenetik ; Bioinformatik ; Statistischer Test ; Funktionelle Kernspintomografie
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (12 Seiten, 218,03 KB) , Illustrationen, Diagramme
    Language: German
    Note: Förderkennzeichen BMBF 031A191H. - Verbund-Nummer 01137806 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden
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  • 4
    Online Resource
    Online Resource
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Description / Table of Contents: Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family in this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (35 Seiten, 3.873 kB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik No. 2127
    Language: English
    Note: Literaturverzeichnis: Seite 16-19
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  • 5
    Keywords: Forschungsbericht
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 34 S., 277 KB)
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1913
    Language: English
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  • 6
    Online Resource
    Online Resource
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik Leibniz-Institut im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (17 Seiten, 2,93 MB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik no. 2806
    Language: English
    Note: Literaturverzeichnis: Seite 12-15
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  • 7
    Online Resource
    Online Resource
    Berlin : Weierstraß-Inst. für Angewandte Analysis und Stochastik Leibniz-Inst. im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Description / Table of Contents: We are concerned with statistical inference for 2 x 2 x K contingency tables in the context of genetic case-control association studies. Multivariate methods based on asymptotic Gaussianity of vectors of test statistics require information about the asymptotic correlation structure among these test statistics under the global null hypothesis. We show that for a wide variety of test statistics this asymptotic correlation structure is given by the linkage disequilibrium matrix of the K loci under investigation. Three popular choices of test statistics are discussed for illustration.
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 11 S., 212 KB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 2029
    Language: English
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  • 8
    Online Resource
    Online Resource
    Berlin : Weierstraß-Inst. für Angewandte Analysis und Stochastik Leibniz-Inst. im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Description / Table of Contents: Genetic association studies lead to simultaneous categorical data analysis. The sample for every genetic locus consists of a contingency table containing the numbers of observed genotype-phenotype combinations. Under case-control design, the row counts of every table are identical and fixed, while column counts are random. The aim of the statistical analysis is to test independence of the phenotype and the genotype at every locus. We present an objective Bayesian methodology for these association tests, utilizing the Bayes factor proposed by Good (1976) and Crook and Good (1980). It relies on the conjugacy of Dirichlet and multinomial distributions, where the hyperprior for the Dirichlet parameter is log-Cauchy. Being based on the likelihood principle, the Bayesian tests avoid looping over all tables with given marginals. Hence, their computational burden does not increase with the sample size, in contrast to frequentist exact tests. Making use of data generated by The Wellcome Trust Case Control Consortium (2007), we illustrate that the ordering of the Bayes factors shows a good agreement with that of frequentist p-values. Furthermore, we deal with specifying prior probabilities for the validity of the null hypotheses, by taking linkage disequilibrium structure into account and exploiting the concept of effective numbers of tests. Application of a Bayesian decision theoretic multiple test procedure to The Wellcome Trust Case Control Consortium (2007) data illustrates the proposed methodology. Finally, we discuss two methods for reconciling frequentist and Bayesian approaches to the multiple association test problem for contingency tables in genetic association studies.
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 18 S., 283 KB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1995
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Systemvoraussetzungen: Acrobat reader.
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  • 9
    Online Resource
    Online Resource
    Berlin : Weierstraß-Inst. für Angewandte Analysis und Stochastik Leibniz-Inst. im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Description / Table of Contents: We consider computational methods for evaluating and approximating multivariate chi-square probabilities in cases where the pertaining correlation matrix or blocks thereof have a low-factorial representation. To this end, techniques from matrix factorization and probability theory are applied. We outline a variety of statistical applications of multivariate chi-square distributions and provide a system of MATLAB programs implementing the proposed algorithms. Computer simulations demonstrate the accuracy and the computational efficiency of our methods in comparison with Monte Carlo approximations, and a real data example from statistical genetics illustrates their usage in practice.
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 19 S., 257 KB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 2005
    Language: English
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  • 10
    Online Resource
    Online Resource
    Berlin : Weierstraß-Inst. für Angewandte Analysis und Stochastik Leibniz-Inst. im Forschungsverbund Berlin e.V.
    Keywords: Forschungsbericht
    Description / Table of Contents: Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 19 S., 280 KB)
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 2065
    Language: English
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