Results 221 to 230 of about 14,911 (257)

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 ...
openaire   +1 more source

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 ...
openaire   +2 more sources

Inference for heavy tailed distributions

Journal of Statistical Planning and Inference, 1998
This work develops the statistical inference for stable laws of order \(\alpha\) and an asymmetry parameter \(\beta\), based on an independent sample of size \(n\geq 1\). Three different approaches to the construction of confidence intervals for the mean \(\mu\) are proposed, two of them involving bootstrap.
Athreya, K. B., Lahiri, S. N., Wu, Wei
openaire   +1 more source

Heavy-tailed distributions and multi-keyword queries

Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Intersecting inverted indexes is a fundamental operation for many applications in information retrieval and databases. Efficient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval applications often have convenient power-law constraints (also known as Zipf's ...
Surajit Chaudhuri, Kenneth Ward Church, Arnd Christian König, Liying Sui   +3 more
openaire   +1 more source

Approximating expected shortfall for heavy-tailed distributions

Econometrics and Statistics, 2018
A saddlepoint approximation for evaluating the expected shortfall of financial returns under realistic distributional assumptions is derived. This addresses a need that has arisen after the Basel Committee’s proposed move from Value at Risk to expected shortfall as the mandated risk measure in its market risk framework.
Broda, Simon A, Krause, Jochen, Paolella, Marc S   +2 more
openaire   +4 more sources

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
openaire   +1 more source

Random Walk with a Heavy-Tailed Jump Distribution

Queueing Systems, 2002
Frm the author's abstract: The classical random walk of which the one-step displacement variable \({\mathbf u}\) has a first finite negative moment is considered. It is assumed that the right-hand and/or the left-hand tail of the distribution of \({\mathbf u}\) are heavy-tailed.
openaire   +2 more sources

The Weighted Bootstrap Mean for Heavy-Tailed Distributions

Journal of Theoretical Probability, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
del Barrio, E., Matrán, C.
openaire   +1 more source

Transformation of distributions into heavy tailed

SPIE Proceedings, 2016
We consider the transformation of the Raleigh distribution into a new distribution so that the new distribution behaves approximately the same as the Rayleigh for small values of the argument but becomes heavy tailed for large values.
openaire   +1 more source

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
openaire   +1 more source