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Activity Number: 162 - SBSS Student Paper Award Session - I
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #304461
Title: A New Class of Unimodal, Asymmetric, Heavy-Tailed Densities with Applications to Regression and Time-Series Models
Author(s): Li Kang*
Companies: University of Texas At Austin
Keywords: Auxiliary variable; Bayesian inference; Markov chain Monte Carlo; Mixtures of uniforms

In many applications, data often exhibit specic behavior, namely unimodality, skewness and heavy tails. In this paper, we present a new, parsimonious family of distributions which models such features. The manner in which this is done, we show, provides explicit interpretation for all parameters in terms of location, shape, skewness and scale. This is important and relevant, in particular, to facilitate Bayesian inference. In addition to proving useful consistency properties, the theoretical ideas are applied to broad classes of models of interest to practitioners, using real and simulated data.

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

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