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A graphical diagnostic for heavy tailed data
Applied Stochastic Models in Business and Industry, 2020AbstractGraphical diagnostics are described for general heavy tailed data. This tool allows for a model free assessment of the tails of a univariate dataset a transform on the tails of the data. In addition, one can add to the basic plots comparisons of a dataset to multiple models.
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Gumbel distribution with heavy tails and applications to environmental data
Mathematics and Computers in Simulation, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yolanda M. Gómez +2 more
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Data analysis for heavy tailed multivariate samples
Communications in Statistics. Stochastic Models, 1997Summary: We propose methods of exploratory data analysis that test for joint stability of a multivariate data set. Examples are shown of how these techniques support or reject the joint stability of the sample. If these methods suggest that a data set is jointly stable, we would like to know how well methods of estimating a spectral measure work.
Nolan, John P., Panorska, Anna K.
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Flexible Heavy Tailed Distributions for Big Data
Annals of Data Science, 2017The Pareto type I distribution (also known as the power law distribution and Zipf’s law) appears to be the main distribution used to model heavy tailed phenomena in the big data literature. The Pareto type I distribution being one of the oldest heavy tailed distributions is not very flexible.
Yuanyuan Zhang, Saralees Nadarajah
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Private least absolute deviations with heavy-tailed data
Theoretical Computer SciencezbMATH Open Web Interface contents unavailable due to conflicting licenses.
Di Wang 0015, Jinhui Xu 0001
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Synthetic Data via Quantile Regression for Heavy-Tailed and Heteroskedastic Data
2018Privacy protection of confidential data is a fundamental problem faced by many government organizations and research centers. It is further complicated when data have complex structures or variables with highly skewed distributions. The statistical community addresses general privacy concerns by introducing different techniques that aim to decrease ...
Michelle Pistner +2 more
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Bootstrapping Heavy-Tailed Data and Extremes
2003In this chapter, we consider two topics, viz., bootstrapping heavy-tailed time series data and bootstrapping the extremes (i.e., the maxima and the minima) of stationary processes. We call a random variable heavy-tailed if its variance is infinite. For iid random variables with such heavy tails, it is well known (cf.
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Estimating the Conditional Tail Expectation of Randomly Right-Censored Heavy-Tailed Data
Journal of Statistical Theory and PracticezbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nour Elhouda Guesmia +2 more
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Heavy-tailed distributions for building stock data
Environment and Planning B: Urban Analytics and City Science, 2018The question of inferring the owner of a set of building stocks (e.g. from which country the buildings are taken) from building-related quantities like number of buildings or types of building event histories necessitates the knowledge of their distributions in order to compare them.
Patrick Erik Bradley, Martin Behnisch
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Fitting and Predicting Heavy Tailed Insurance Data
SSRN Electronic Journal, 2008Predicting the severity of insurance claims is not difficult when there is correspondence between the observed data and a chosen parametric model. However, for heavy tailed insurance data it is hard to find a parametric model that is sophisticated enough to both predict the tail behavior and fit the body of the data. In the litterature it seems to be a
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