The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
Rainer Spang - One of the best experts on this subject based on the ideXlab platform.
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A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Stefanie Scheid, Rainer SpangAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying uncertainty in multiple testing. In this paper, we introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p{\hbox{-}}{\rm values} of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.
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A stochastic downhill search algorithm for estimating the local false discovery rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Stefanie Scheid, Rainer SpangAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying uncertainty in multiple testing. We introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p-values of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.
Stefanie Scheid - One of the best experts on this subject based on the ideXlab platform.
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A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Stefanie Scheid, Rainer SpangAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying uncertainty in multiple testing. In this paper, we introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p{\hbox{-}}{\rm values} of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.
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A stochastic downhill search algorithm for estimating the local false discovery rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Stefanie Scheid, Rainer SpangAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying uncertainty in multiple testing. We introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p-values of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.
Spangrainer - One of the best experts on this subject based on the ideXlab platform.
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A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Scheidstefanie, SpangrainerAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying un...
Scheidstefanie - One of the best experts on this subject based on the ideXlab platform.
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A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2004Co-Authors: Scheidstefanie, SpangrainerAbstract:Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a Statistical Concept for quantifying un...
A.c. Bovik - One of the best experts on this subject based on the ideXlab platform.
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On the Statistical optimality of locally monotonic regression
IEEE Transactions on Signal Processing, 1994Co-Authors: Restrepo A. Palacios, A.c. BovikAbstract:Locally monotonic regression is a recently proposed technique for the deterministic smoothing of finite-length discrete signals under the smoothing criterion of local monotonicity. Locally monotonic regression falls within a general framework for the processing of signals that may be characterized in three ways: regressions are given by projections that are determined by semimetrics, the processed signals meet shape constraints that are defined at the local level, and the projections are optimal Statistical estimates in the maximum likelihood sense. the authors explore the relationship between the geometric and deterministic Concept of projection onto (generally nonconvex) sets and the Statistical Concept of likelihood, with the object of characterizing projections under the family of the p-semi-metrics as maximum likelihood estimates of signals contaminated with noise from a well-known family of exponential densities.
