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Transfer Learning in Multiple Hypothesis Testing [PDF]
EntropyIn this investigation, a synthesis of Convolutional Neural Networks (CNNs) and Bayesian inference is presented, leading to a novel approach to the problem of Multiple Hypothesis Testing (MHT). Diverging from traditional paradigms, this study introduces a
Stefano Cabras +1 more
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Optical quantum super-resolution imaging and hypothesis testing [PDF]
Nature Communications, 2022 Estimating the angular separation between two incoherent sources below the diffraction limit is challenging. Hypothesis testing and quantum state discrimination techniques are used to super-resolve sources of different brightness with a simple optical ...
Ugo Zanforlin +5 more
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Large Deviation Analysis of Score-Based Hypothesis Testing [PDF]
IEEE AccessScore-based statistical models play an important role in modern machine learning, statistics, and signal processing. For hypothesis testing, a score-based hypothesis test is proposed in Wu et al., (2022).
Enmao Diao +2 more
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Hypothesis testing using R [PDF]
Digital Diagnostics, 2023 Competencies in statistical data processing are becoming increasingly important for modern scientists. The apparent advantages of open-source software for statistical analysis are its accessibility and adaptability.
Ivan A. Blokhin +4 more
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Testing the superstar firm hypothesis
Journal of Applied Economics, 2022 Firms with superior productivity, labeled superstar firms, are argued to be the link between rising concentration and the fall of the aggregate labor share in the US.
Caroline Stiel, Alexander Schiersch
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Instance-Based Classification Through Hypothesis Testing
IEEE Access, 2021 Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as
Zengyou He +3 more
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Hypothesis Testing in Econometrics [PDF]
SSRN Electronic Journal, 2009 This article reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, we summarize some of the most important methods, as well as resampling methodology, which is useful to set ...
Romano, Joseph P +2 more
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Testing the Omnivore Hypothesis in Russia
Monitoring Obŝestvennogo Mneniâ: Ekonomičeskie i Socialʹnye Peremeny, 2021 In the last thirty years, a significant shift from the homology to omnivore argument has occurred in musical preference studies. Studies on the omnivore argument mainly come from North and South America, Western and sometimes Eastern Europe.
Юлия Олеговна Папушина
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Privacy-Aware Distributed Hypothesis Testing
Entropy, 2020 A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless source, and communicates its observations to the detector via a rate-limited
Sreejith Sreekumar +2 more
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Quantum hypothesis testing in many-body systems
SciPost Physics Core, 2021 One of the key tasks in physics is to perform measurements in order to determine the state of a system. Often, measurements are aimed at determining the values of physical parameters, but one can also ask simpler questions, such as "is the system in ...
Jan de Boer, Victor Godet, Jani Kastikainen, Esko Keski-Vakkuri
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