Abstract Details
Activity Number:
|
505
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Nonparametric Statistics
|
Abstract #312370
|
View Presentation
|
Title:
|
The Heavy Tailed Inverse Convex Shape Constraint
|
Author(s):
|
Clifford Anderson-Bergman*+
|
Companies:
|
University of California, Irvine
|
Keywords:
|
Shape Constraints ;
Inverse-Convex ;
Log-Concave ;
Non-parametric ;
Density Estimation
|
Abstract:
|
The log-concave shape constrained density estimator has received much attention in recent years, providing consistent density estimation without the need to select a parametric distribution or smoothing parameters. However, for heavy tailed data, the log-concave assumption may be inappropriate, as it allows only up to exponential tails. To address this concern, we present a new shape constraint we call inverse convex. We show that the family of log-concave distributions fits properly into the family of inverse convex and that inverse convex also allows for heavier tails. We present other basic properties of the family of inverse convex distributions, interesting characteristics of the inverse convex estimator (including an unbounded likelihood function), and apply the new estimator to fit income data.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.