Graphical representations and associated goodness-of-fit tests for Pareto and log-normal distributions based on inequality curves
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Publication:5023855
DOI10.1080/10485252.2021.1977300OpenAlexW3201460783MaRDI QIDQ5023855
Giuseppe Espa, Flavio Santi, Emanuele Taufer
Publication date: 25 January 2022
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485252.2021.1977300
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- Goodness-of-fit tests for the Pareto distribution based on its characterization
- On the controversy over tailweight of distributions.
- A unified approach of testing for discrete and continuous Pareto laws
- Are your data really Pareto distributed?
- Semi-parametric regression estimation of the tail index
- A test of goodness-of-fit based on Gini's index of spacings
- Discriminating between the log-normal and generalized exponential distributions
- The qq-estimator and heavy tails
- Goodness-of-fit tests for Pareto distribution based on a characterization and their asymptotics
- Asymptotic properties of the partition function and applications in tail index inference of heavy-tailed data
- A Powerful Method of Assessing the Fit of the Lognormal Distribution
- Power-Law Distributions in Empirical Data
- New Goodness-of-Fit Tests for Pareto Distributions
- Convergence theorems for empirical Lorenz curves and their inverses
- Entropy-based goodness-of-fit tests for the Pareto I distribution
- Discriminating among Weibull, log-normal, and log-logistic distributions
- Discriminating Between the Log-Normal and Log-Logistic Distributions
- Estimating test power adjusted for size
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