p-value based statistical significance tests: Concepts, misuses, critiques, solutions and beyond [PDF]
Computational Ecology and Software, 2022 The p-value is at the heart of statistical significance tests, a very important issue related to the role of statistical inference in advancing scientific discovery. Over the past few decades, p-value based statistical significance tests have been widely
WenJun Zhang
doaj
Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods [PDF]
E3S Web of Conferences, 2023 High uncertainties arias through the characterization of soil parameters because of the lack of data obtained from geotechnical reports. Reducing these uncertainties may improve the characteristic values of soil parameters.
Abd Al-Haleem Zainab, Shakir Ressol R.
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The Role of Digital Media in Shaping Youth Planetary Health Interests in the Global Economy
Journal of Open Innovation: Technology, Market and Complexity, 2020 Despite revolutionizing the work of practicing economists by providing a direct link between neo-classical economic theory and revealed market preference data, Random Utility Theory has yet to guide research applications in global market sustainability ...
Ibrahim Niankara +2 more
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A system for determining maximum tolerated dose in clinical trial
Statistical Theory and Related Fields, 2021 Toxicity study, especially in determining the maximum tolerated dose (MTD) in phase I clinical trial, is an important step in developing new life-saving drugs.
Keying Ye +3 more
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Consciously Uncertain: A Bayesian Analysis of Preferences Formation
Games, 2022 We investigate experimentally whether players deliberately use irrelevant market cues to shape their evaluations of a traded item. We implement a repeated Vickrey median price selling auction of an unusual bad where players are informed on the market ...
Marco Stimolo +3 more
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Beyond ‘significance’: principles and practice of the Analysis of Credibility [PDF]
Royal Society Open Science, 2018 The inferential inadequacies of statistical significance testing are now widely recognized. There is, however, no consensus on how to move research into a ‘post p
Robert A. J. Matthews
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Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
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Bayesian Probabilistic Numerical Methods [PDF]
SIAM Review, 2019 The emergent field of probabilistic numerics has thus far lacked clear statistical principals. This paper establishes Bayesian probabilistic numerical methods as those which can be cast as solutions to certain inverse problems within the Bayesian framework.
Jon Cockayne +3 more
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Classification of chirp signals using hierarchical bayesian learning and MCMC methods [PDF]
, 2002 This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods.
Davy, Manuel +5 more
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Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools
Sensors, 2019 A new software package, ISEtools, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner.
Peter W. Dillingham +3 more
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