Results 241 to 250 of about 233,260 (285)

Box–Cox transformations and heavy-tailed distributions

Journal of Applied Probability, 2004
It is a stylized fact that estimators in extreme-value theory suffer from serious bias. Moreover, graphical representations of extremal data often show erratic behaviour. In the statistical literature it is advised to use a Box–Cox transformation in order to make data more suitable for statistical analysis. We provide some of the theoretical background
Teugels, Jef L., Vanroelen, Giovanni
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Distributions with Heavy Tails in Orlicz Spaces

Journal of Theoretical Probability, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Konstantinides, Dimitrios G., Kountzakis, Christos E.   +1 more
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Structural Equation Modeling with Heavy Tailed Distributions

Psychometrika, 2004
Data in social and behavioral sciences typically possess heavy tails. Structural equation modeling is commonly used in analyzing interrelations among variables of such data. Classical methods for structural equation modeling fit a proposed model to the sample covariance matrix, which can lead to very inefficient parameter estimates.
Yuan, Ke-Hai, Bentler, Peter M., Chan, Wai   +2 more
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Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management

World Scientific Handbook in Financial Economics Series, 2019
The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their ...
M. L. Bianchi, Stoyan Stoyanov, G. Tassinari, F. Fabozzi, S. Focardi   +4 more
semanticscholar   +1 more source

Performance Analysis with Truncated Heavy-Tailed Distributions

Methodology and Computing in Applied Probability, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Asmussen, S., Pihlsgård, M.
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Estimating the Mean of Heavy-Tailed Distributions

Extremes, 2003
For i.i.d. observations \(X=(X_1,\dots,X_n)\) with CDF \[ F(x)=1-cx^{-1/\xi}(1+x^{-\delta}L(x)) \] (\(L\) being a slowly varying function) the problem of mean \({\mathbf E}X_1\) estimation is considered in the case \(\xi\in(1/2,1)\). (For \(\xi\in (0,1/2)\) the sample mean is an asymptotically normal estimate of \({\mathbf E}X_1\), for \(\xi>1\) the ...
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Latest developments on heavy-tailed distributions

Journal of Econometrics, 2013
The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. In particular, non-causal representations are not identified in the case of Gaussian AR processes.
Paolella, Marc, Renault, Eric, Samorodnitsky, Gennady, Veredas, David   +3 more
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Heavy Tail Distributions

2013
Motivated by the instances of extreme events and heavy tail distributions encountered in the first chapter, we present the most important theoretical results underpinning the estimation of the probabilities of these extreme and rare events. The basics of extreme value theory are presented as they pertain to estimation and risk management of extremes ...
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Estimation of extreme quantiles from heavy-tailed distributions with neural networks

Statistics and computing, 2023
Michael Allouche, S. Girard, E. Gobet
semanticscholar   +1 more source

Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms

Journal of Business Venturing
This study extends emerging theories of star performers to digital platforms, an increasingly prevalent entrepreneurial context. It hypothesizes that the unique characteristics of many digital platforms (e.g., low marginal costs, feedback loops, and ...
Kaushik Gala, Andreas Schwab, Brandon A. Mueller   +2 more
semanticscholar   +1 more source