ACCC+commissioned+report+-+The+impact+of+digital+platforms+
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Point Source Detection and False Discovery Rate Control on CMB
Feb 18 2019 We validate our procedure on realistic simulations of CMB maps and
Point Source Detection and False Discovery Rate Control on CMB
Jul 26 2019 We discuss the STEM (Smoothing and Testing Multiple hypotheses) procedure to search for point sources in Cosmic. Microwave background maps; in ...
ORIGIN: Blind detection of faint emission line galaxies in MUSE
Feb 1 2020 particular with respect to their actual false detection rate
ORIGIN: Blind detection of faint emission line galaxies in MUSE
Jul 21 2020 lar with respect to their actual false detection rate
Searching for Binary Asteroids in Pan-STARRS1 Archival Images
Jul 20 2022 We then applied these techniques to images of main belt asteroids in ... curve discovery rates in the main belt have been consistent with.
Physical Evidence for Dark Energy
Jul 20 2003 of the ISW effect provides independent physical evidence for the existence of ... ing the False Discovery Rate (FDR [16
Onboard catalogue of known X-ray sources for SVOM/ECLAIRs
Oct 19 2020 the quality of the sky image reconstruction process. ... order to ensure the detection of GRBs with a low false-alarm rate.
Searching for ring-like structures in the Cosmic Microwave
May 9 2021 code of the algorithm to scan the CMB sky maps (Sec- ... dictive Value (PPV) and False Discovery Rate (FDR).
A search for Galactic transients disguised as gamma-ray bursts
MEPSA which ensures a negligible false positive rate for our obtained from MC simulations (method 1; histograms) and from the sky pixelisation in the.
False discovery rate smoothing - arXivorg
This paper presents a new method called false discovery rate smoothing that can learn and exploit the underlying spatial structures in these multiple-testing problems FDR smoothing ?nds spatially localized regions of signi?cant test statistics by solving a speci?c optimization problem involving the ‘ 1 penalty It then relaxes the
A direct approach to false discovery rates - Princeton University
In this paper we propose a new approach to false discovery rates We attempt to use more traditionalandstraightforwardstatisticalideastocontrolpFDRandFDR Insteadof?xing? and then estimating k (i e estimating the rejection region) we ?x the rejection region and then estimate?
A Tutorial on False Discovery Control
The False Discovery Rate (FDR) for a multiple testing threshold T is de ned as the expected FDP using that procedure: FDR = E FDP(T) Aside: The False Non-Discovery Rate We can de ne a dual quantity to the FDR the False Nondiscovery Rate (FNR)
Understanding and Extending the Controlling the False
Controlling the False Discovery Rate: Understanding and Extending the Benjamini-Hochberg Method Christopher R Genovese Department of Statistics Carnegie Mellon University joint work with Larry Wasserman This work partially supported by NSF Grant SES 9866147
A generalizable method for false-discovery rate estimation in
Feb 18 2020 · proteomics a variety of methods for estimating false-discovery are available and understanding the statistical confidence of identifications is typically required for data analysis and hypothesis testing However there is no current method for estimating the false discovery rate (FDR) or statistical confidence for MS-based lipid identifications
False Discovery Rates - Princeton University
provided the rst implementation of false discovery rates with known operating characteristics The idea of quantifying the rate of false discoveries is directly related to several pre-existing ideas such as Bayesian misclassi cation rates and the positive predictive value (Storey 2003)
Astronomical Transient Detection using Grouped p-Values and
The False Discovery Rate (FDR) proposed by Benjamini and Hochberg (1995) is the expected proportion of Type I errors among all the rejected null hypotheses It is now a widely ac- cepted notion of error rate to control in large-scale multiple testings arising in modern scienti?c investigations including astronomical source detection
False Discovery Rate Regression: An Application to - JSTOR
The method we describe here builds on the two-groups model (Efron et al 2001) an intuitive framework for controlling the false-discovery rate In the two-groups model some small frac tion c of the test statistics are assumed to come from an unknown signal population and the remainder from a known null popu
How is the false discovery rate (FDR) controlled?
- For example, controlling the false discovery rate FDR traditionally involves intricate sequential p-value rejection methods based on the observed data.
What is the difference between false discovery rate and ratea prioriat?
- 482J. D. Storey or more type I error, which is essentially a ‘0–1’ event, we can set the ratea prioriat which this should occur. False discovery rates, in contrast, are more of an exploratory tool. For example, suppose that we are testing 1000 hypotheses and decide beforehand to control FDR at level 5%.
What is the false discov-ery rate?
- This false discov- ery rate is robust to the false positive paradox and is particularly useful in exploratory analyses, where one is more concerned with having mostly true fndings among a set of statistically signifcant discoveries rather than guarding against one or more false positives.
Should we truncate the estimates at 1 since FDR pFDR 1?
- 488J. D. Storey truncate the estimates at 1 since FDR pFDR 1, but the expected value of the estimates nevertheless behaves as we would want it to. The following result shows that truncating the estimates is a good idea when taking into account both bias and variance. Theorem 3. E[{pFDR
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