772 research outputs found
Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
Multiple hypothesis testing is a fundamental problem in high dimensional
inference, with wide applications in many scientific fields. In genome-wide
association studies, tens of thousands of tests are performed simultaneously to
find if any SNPs are associated with some traits and those tests are
correlated. When test statistics are correlated, false discovery control
becomes very challenging under arbitrary dependence. In the current paper, we
propose a novel method based on principal factor approximation, which
successfully subtracts the common dependence and weakens significantly the
correlation structure, to deal with an arbitrary dependence structure. We
derive an approximate expression for false discovery proportion (FDP) in large
scale multiple testing when a common threshold is used and provide a consistent
estimate of realized FDP. This result has important applications in controlling
FDR and FDP. Our estimate of realized FDP compares favorably with Efron
(2007)'s approach, as demonstrated in the simulated examples. Our approach is
further illustrated by some real data applications. We also propose a
dependence-adjusted procedure, which is more powerful than the fixed threshold
procedure.Comment: 51 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:1012.439
The Isotonic Mechanism for Exponential Family Estimation
In 2023, the International Conference on Machine Learning (ICML) required
authors with multiple submissions to rank their submissions based on perceived
quality. In this paper, we aim to employ these author-specified rankings to
enhance peer review in machine learning and artificial intelligence conferences
by extending the Isotonic Mechanism (Su, 2021, 2022) to exponential family
distributions. This mechanism generates adjusted scores closely align with the
original scores while adhering to author-specified rankings. Despite its
applicability to a broad spectrum of exponential family distributions, this
mechanism's implementation does not necessitate knowledge of the specific
distribution form. We demonstrate that an author is incentivized to provide
accurate rankings when her utility takes the form of a convex additive function
of the adjusted review scores. For a certain subclass of exponential family
distributions, we prove that the author reports truthfully only if the question
involves only pairwise comparisons between her submissions, thus indicating the
optimality of ranking in truthful information elicitation. Lastly, we show that
the adjusted scores improve dramatically the accuracy of the original scores
and achieve nearly minimax optimality for estimating the true scores with
statistical consistecy when true scores have bounded total variation
PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection
With the rapid development of 3D printing technology, 3D printers are manufactured based on the principle of 3D printing technology are more and more widely used in the manufacturing industry. Choosing high quality 3D printers for industrial production is of great significance to the economic growth of enterprises. In fact, it is difficult to select the most optimal 3D printers under a single and simple standard. Therefore, this paper establishes the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for the multiple attribute group decision making (MAGDM). Then the CRITIC model is introduced to derive objective weight and the cumulative prospect theory is leaded into obtain the cumulative weight of PDHLTS. In addition, what’s more, the PDHL-EDAS method is built and applied to the choice of high-quality 3D printer. Finally, compared with the available MAGDM methods under PDHLTS, the built method is proved to be scientific and effective.
First published online 15 December 202
Radar-assisted Predictive Beamforming for Vehicle-to-Infrastructure Links
In this paper, we propose a radar-assisted predictive beamforming design for
vehicle-to-infrastructure (V2I) communication by relying on the joint sensing
and communication functionalities at road side units (RSUs). We present a novel
extended Kalman filtering (EKF) framework to track and predict kinematic
parameters of the vehicle. By exploiting the radar functionality of the RSU we
show that the communication beam tracking overheads can be drastically reduced.
Numerical results have demonstrated that the proposed radar-assisted approach
significantly outperforms the communication-only feedback based technique in
both the angle tracking and the downlink communication.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2020. arXiv admin note:
substantial text overlap with arXiv:2001.0930
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