|
Activity Number:
|
549
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Risk Analysis
|
| Abstract - #305893 |
|
Title:
|
Approximate Bayesian Computation for Flexible Quantile Distributions
|
|
Author(s):
|
Robert King*+
|
|
Companies:
|
Newcastle University
|
|
Address:
|
, , ,
|
|
Keywords:
|
|
|
Abstract:
|
The generalized lambda and g-and-k distributions are Quantile distributions that allow a very wide range of shapes within one distributional form. These distributions are defined by their quantile function and rarely have analytical likelihood functions defined. Bayesian methodologies such as Gibbs sampling cannot be applied to parameter estimation for this valuable class of distributions without resorting to numerical inversion. Approximate Bayesian computation provides an alternative approach requiring only a sampling scheme for the distribution of interest, enabling easier use of quantile distributions under the Bayesian framework. Parameter estimates for simulated and experimental data are presented.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2009 program |
