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Bayesian analysis of jointly heavy-tailed data [PDF]
, 2022 This thesis develops novel Bayesian methodologies for statistical modelling of heavy-tailed data. Heavy tails are often found in practice, and yet they are an Achilles heel of a variety of main-stream random probability measures such as the Dirichlet process.
Palacios Ramırez, Karla Vianey
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Graph Learning for Balanced Clustering of Heavy-Tailed Data
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023 Graph learning is an emerging research area with many applications in machine learning, specifically data clustering. There are numerous algorithms for learning a graph, among which, Gaussian models have gained significant attraction.
Amirhossein Javaheri +2 more
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On Model-Based Clustering of Directional Data with Heavy Tails
Journal of Classification, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yingying Zhang +2 more
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Communications in Statistics - Simulation and Computation, 2020
Heavy tailed distributions play very significant role in the study of actuarial and financial risk management data but the probability distributions proposed to model such data are scanty.
Zubair Ahmad 0006 +2 more
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Heavy tailed distributions play very significant role in the study of actuarial and financial risk management data but the probability distributions proposed to model such data are scanty.
Zubair Ahmad 0006 +2 more
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Anytime Guarantees under Heavy-Tailed Data
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Under data distributions which may be heavy-tailed, many stochastic gradient-based learning algorithms are driven by feedback queried at points with almost no performance guarantees on their own. Here we explore a modified "anytime online-to-batch" mechanism which for smooth objectives admits high-probability error bounds while requiring only lower ...
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Robust Nonparametric Regression for Heavy-Tailed Data
Journal of Agricultural, Biological and Environmental Statistics, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ferdos Gorji, Mina Aminghafari
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Body tail adaptive kernel density estimation for nonnegative heavy-tailed data
Monte Carlo Methods and Applications, 2021Abstract In this paper, we consider the procedure for deriving variable bandwidth in univariate kernel density estimation for nonnegative heavy-tailed (HT) data. These procedures consider the Birnbaum–Saunders power-exponential (BS-PE) kernel estimator and the bayesian approach that treats the adaptive bandwidths.
Yasmina Ziane +2 more
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On the favorable estimation for fitting heavy tailed data
Computational Statistics, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Milan Stehlík +3 more
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Models for Heavy Tailed Data and Applications
AIP Conference Proceedings, 2008An important topic in space research is represented by the study of statistical properties of the interplanetary magnetic field fluctuations, these being closely related to acceleration processes and energy transport in the solar wind. Analysis of the probability distribution functions of the velocity and magnetic field fluctuations has underlined ...
Emil Popescu +4 more
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On fuzzy clustering for heavy-tailed data
2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 2017The fuzzy c-means method is investigated to cluster the heavy tailed data by using some measures of distance. A comparison study is provided based on time and precision. The results show that when using the Euclidean distance, the time required is less than if we used Manhattan distance, but the precision is higher when using the Manhattan distance.
S. Mahmoud Taheri +2 more
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