Adapting to unknown sparsity by controlling the false discovery rate

Abramovich, Felix; Benjamini, Yoav; Donoho, David L.; Johnstone, Iain M.

doi:10.1214/009053606000000074

Cited by 359 publications

(487 citation statements)

References 48 publications

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“…To compare models with such a wide range of variables to include, we opted to use an adaptive model selection criterion controlling the FDR [13,14] developed for this purpose. The multilocus models used during backward elimination were implemented in a standard linear model framework, starting with a full model including the fixed effects of sex and the additive effects of the 99 selected markers from the independently segregating regions.…”

Section: Selection Of Markers To Represent the Independently Segregatmentioning

confidence: 99%

Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

Sheng

Pettersson

Honaker

et al. 2015

Preprint

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Background: Artificial selection provides a powerful approach to study the genetics of adaptation. Using selectivesweep mapping, it is possible to identify genomic regions where allele-frequencies have diverged during selection. To avoid false positive signatures of selection, it is necessary to show that a sweep affects a selected trait before it can be considered adaptive. Here, we confirm candidate, genome-wide distributed selective sweeps originating from the standing genetic variation in a long-term selection experiment on high and low body weight of chickens. Results: Using an intercross between the two divergent chicken lines, 16 adaptive selective sweeps were confirmed based on their association with the body weight at 56 days of age. Although individual additive effects were small, the fixation for alternative alleles across the loci contributed at least 40 % of the phenotypic difference for the selected trait between these lines. The sweeps contributed about half of the additive genetic variance present within and between the lines after 40 generations of selection, corresponding to a considerable portion of the additive genetic variance of the base population. Conclusions: Long-term, single-trait, bi-directional selection in the Virginia chicken lines has resulted in a gradual response to selection for extreme phenotypes without a drastic reduction in the genetic variation. We find that fixation of several standing genetic variants across a highly polygenic genetic architecture made a considerable contribution to long-term selection response. This provides new fundamental insights into the dynamics of standing genetic variation during long-term selection and adaptation.

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Section: Selection Of Markers To Represent the Independently Segregatmentioning

confidence: 99%

Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

Sheng

Pettersson

Honaker

et al. 2015

Preprint

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“…Here we wish to mention also the control of error rate thresholding developed by Abramovich and Benjamini [1], Benjamini and Liu [7], and Abramovich et al [2].…”

Section: Multipoint Estimationmentioning

confidence: 99%

“…where the weights are deÞned as in (2). Recall thatĈ 1 in (35) is an estimate of the derivative ∂y(x 0 )/∂x.…”

Section: Nonlocal Pointwise Higher-order Modelsmentioning

confidence: 99%

From Local Kernel to Nonlocal Multiple-Model Image Denoising

et al. 2009

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We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain Þltering based on nonlocal block-matching. The considered methods are classiÞed mainly according to two main features: local/nonlocal and pointwise/multipoint. Here nonlocal is an alternative to local, and multipoint is an alternative to pointwise. These alternatives, though obvious simpliÞcations, allow to impose a fruitful and transparent classiÞcation of the basic ideas in the advanced techniques. Within this framework, we introduce a novel single-and multiplemodel transform domain nonlocal approach. The Block Matching and 3-D Filtering (BM3D) algorithm, which is currently one of the best performing denoising algorithms, is treated as a special case of the latter approach.

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“…These include compressive sensing, [3], [4], adaptive testing [5], [6], and adaptive sampling [7], [8]. The contribution of this paper is most closely related to the adaptive sampling problem.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Multiscale Search for Sparse Targets

Bashan

Newstadt

Hero

2011

IEEE Trans. Signal Process.

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Abstract-We consider the problem of energy constrained and noise-limited search for targets that are sparsely distributed over a large area. We propose a multi-scale search algorithm that significantly reduces the search time of the adaptive resource allocation policy (ARAP) introduced in [Bashan et all, 2008]. Similarly to ARAP, the proposed approach scans a Q-cell partition of the search area in two stages: first the entire domain is scanned and second a subset of the domain, suspected of containing targets, is re-scanned. The search strategy of the proposed algorithm is driven by maximization of a modified version of the previously introduced ARAP objective function, which is a surrogate for energy constrained target detection performance. We analyze the performance of the proposed multistage ARAP approach and show that it can reduce mean search time with respect to ARAP for equivalent energy constrained detection performance. To illustrate the potential gains of M-ARAP, we simulate a moving target indicator (MTI) radar system and show that M-ARAP achieves an estimation performance gain of 7 dB and a 85% reduction in scan time as compared to an exhaustive search. This comes within 1 dB of the previously introduced ARAP algorithm at a fraction of its required scan time.

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Adapting to unknown sparsity by controlling the false discovery rate

Cited by 359 publications

References 48 publications

Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

From Local Kernel to Nonlocal Multiple-Model Image Denoising

Two-Stage Multiscale Search for Sparse Targets

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