2,470 research outputs found
FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control
Large-scale multiple testing with correlated and heavy-tailed data arises in
a wide range of research areas from genomics, medical imaging to finance.
Conventional methods for estimating the false discovery proportion (FDP) often
ignore the effect of heavy-tailedness and the dependence structure among test
statistics, and thus may lead to inefficient or even inconsistent estimation.
Also, the commonly imposed joint normality assumption is arguably too stringent
for many applications. To address these challenges, in this paper we propose a
Factor-Adjusted Robust Multiple Testing (FarmTest) procedure for large-scale
simultaneous inference with control of the false discovery proportion. We
demonstrate that robust factor adjustments are extremely important in both
controlling the FDP and improving the power. We identify general conditions
under which the proposed method produces consistent estimate of the FDP. As a
byproduct that is of independent interest, we establish an exponential-type
deviation inequality for a robust -type covariance estimator under the
spectral norm. Extensive numerical experiments demonstrate the advantage of the
proposed method over several state-of-the-art methods especially when the data
are generated from heavy-tailed distributions. The proposed procedures are
implemented in the R-package FarmTest.Comment: 52 pages, 9 figure
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
We offer a survey of recent results on covariance estimation for heavy-tailed
distributions. By unifying ideas scattered in the literature, we propose
user-friendly methods that facilitate practical implementation. Specifically,
we introduce element-wise and spectrum-wise truncation operators, as well as
their -estimator counterparts, to robustify the sample covariance matrix.
Different from the classical notion of robustness that is characterized by the
breakdown property, we focus on the tail robustness which is evidenced by the
connection between nonasymptotic deviation and confidence level. The key
observation is that the estimators needs to adapt to the sample size,
dimensionality of the data and the noise level to achieve optimal tradeoff
between bias and robustness. Furthermore, to facilitate their practical use, we
propose data-driven procedures that automatically calibrate the tuning
parameters. We demonstrate their applications to a series of structured models
in high dimensions, including the bandable and low-rank covariance matrices and
sparse precision matrices. Numerical studies lend strong support to the
proposed methods.Comment: 56 pages, 2 figure
Research on intelligent fault diagnosis of mechanical equipment based on sparse deep neural networks
In the big data background, the accuracy of fault diagnosis and recognition has been difficult to be improved. The deep neural network was used to recognize the diagnosis rate of the bearing with four kinds of conditions and compared with traditional BP neural network, genetic neural network and particle swarm neural network. Results showed that the diagnosis accuracy and convergence rate of the deep neural network were obviously higher than those of other models. Fault diagnosis rates with different sample sizes and training sample proportions were then studied to compare with the latest reported methods. Results showed that fault diagnosis had a good stability using deep neural networks. Vibration accelerations of the bearing with different fault diameters and excitation loads were extracted. The deep neural network was used to recognize these faults. Diagnosis accuracy was very high. In particular, the fault diagnosis rate was 98Â % when signal features of vibration accelerations were very obvious, which indicated that using deep neural network was effective in diagnosing and recognizing different types of faults. Finally, the deep neural network was used to conduct fault diagnosis for the gearbox of wind turbines and compared with the other models to present that it would work well in the industrial environment
Dark Matter Spike surrounding Supermassive Black Holes Binary and the nanohertz Stochastic Gravitational Wave Background
Recently, the NANOGrav, PPTA, EPTA and CPTA collaborations reported
compelling evidence of the existence of the Stochastic Gravitational-Wave
Background (SGWB). The amplitude and spectrum of this inferred
gravitational-wave background align closely with the astrophysical predictions
for a signal originating from the population of supermassive black-hole
binaries. In light of these findings, we explore the possibility to detect dark
matter spikes surrounding massive black holes, which could potentially impact
the gravitational-wave waveform and modulate the SGWB. We demonstrate that the
SMBH binary evolution induced by the combined effects of GW radiation and the
dynamical friction of the dark matter spike exhibits detectable manifestations
within the nHz frequency range of the SGWB.Comment: 5 pages, 1 figure. arXiv admin note: text overlap with
arXiv:1408.3534 by other author
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