Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis
IEEE Open Journal of Signal Processing, 2022 We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\exp (-n\,I)$
Paolo Braca +5 more
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A Fuzzy Take on the Logical Issues of Statistical Hypothesis Testing
Philosophies, 2021 Statistical Hypothesis Testing (SHT) is a class of inference methods whereby one makes use of empirical data to test a hypothesis and often emit a judgment about whether to reject it or not. In this paper, we focus on the logical aspect of this strategy,
Matthew Booth, Fabien Paillusson
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Quantum Hypothesis Testing and Non-Equilibrium Statistical Mechanics [PDF]
, 2011 We extend the mathematical theory of quantum hypothesis testing to the general $W^*$-algebraic setting and explore its relation with recent developments in non-equilibrium quantum statistical mechanics.
Araki H. +20 more
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Statistical analysis of control quality of MPC using testing hypothesis [PDF]
MATEC Web of Conferences, 2019 Methods of the statistical induction have a significant role in the quantitative research. In a wide spectrum of research areas, the methods based on testing hypotheses have been frequently used.
Kubalcik Marek +2 more
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Universal Codes as a Basis for Time Series Testing [PDF]
, 2005 We suggest a new approach to hypothesis testing for ergodic and stationary processes. In contrast to standard methods, the suggested approach gives a possibility to make tests, based on any lossless data compression method even if the distribution law of
Astola, Jaakko, Ryabko, Boris
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Statistical inference: Hypothesis testing
Allergologia et Immunopathologia, 2010 The aim of statistical inference is to predict the parameters of a population, based on a sample of data. Inferential statistics encompasses the estimation of parameters and model predictions. The present article describes the hypothesis tests or statistical significance tests most commonly used in healthcare research.
M, Expósito-Ruiz +2 more
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Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies [PDF]
, 2011 Rejoinder to "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]Comment: Published in at http://dx.doi.org/10.1214/08-STS244REJ the Statistical Science (http://www.imstat.org/sts/
Kong, Augustine +2 more
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Testing statistical hypothesis on random trees and applications to the protein classification problem [PDF]
, 2009 Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from
Busch, Jorge R. +5 more
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Optimal Testing for Planted Satisfiability Problems [PDF]
, 2015 We study the problem of detecting planted solutions in a random satisfiability formula. Adopting the formalism of hypothesis testing in statistical analysis, we describe the minimax optimal rates of detection.
Berthet, Quentin
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On methods for correcting for the look-elsewhere effect in searches for new physics [PDF]
, 2016 The search for new significant peaks over a energy spectrum often involves a statistical multiple hypothesis testing problem. Separate tests of hypothesis are conducted at different locations producing an ensemble of local p-values, the smallest of which
Algeri, Sara +3 more
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