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Activity Number: 252 - SPEED: Nonparametrics and Imaging
Type: Contributed
Date/Time: Monday, July 31, 2017 : 3:05 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #325195
Title: Nonparametric Modeling of Heavy-Tailed Distributions with Applications to Extreme Events
Author(s): Todd Wilson* and Sujit K Ghosh
Companies: North Carolina State University and North Carolina State University
Keywords: Nonparametrics ; Density estimation ; Heavy-tailed distributions ; Nonlinear optimization

Estimation of heavy-tailed distributions has a long history and such distributions play a fundamental role in estimating the occurrence of extreme events (e.g., very high or cold temperatures, huge earthquakes, storm surge, sudden large fluctuations in stock market). Several parametric classes of models are used to capture the tails of various subclasses of distributions (e.g., fat-tailed, long-tailed and sub-exponential distributions). This paper develops a nonparametric class of distributions based on a mixture of scaled Beta distributions with tail adjustments that is flexible to capture different types of tail behaviors of the underlying distributions. A computationally efficient algorithm is presented that allows for the estimation of the density based on maximizing a sieve of likelihood functions as the number of mixture components grows with sample size. Numerical illustrations are presented with simulated and real data sets.

Authors who are presenting talks have a * after their name.

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