Abstract Details
Activity Number:
|
132
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 5, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Business and Economic Statistics Section
|
Abstract - #309060 |
Title:
|
An Importance Sampling Approach for Exploring Likelihoods of Stochastic Differential Equations
|
Author(s):
|
Grant Schneider*+
|
Companies:
|
OSU
|
Keywords:
|
Stochastic Differential Equations ;
Importance Sampler
|
Abstract:
|
Stochastic Differential Equations (SDEs) are used to model processes from many different disciplines, including finance, biology, and engineering. While these processes are continuously defined, inference is based on data observed at only a finite number of locations. This typically leads to intractable likelihoods, which causes difficulty in statistical inference, as it is impossible to observe a complete path in practice. Two competing methods have been proposed to overcome the problem of intractable likelihoods. The first method develops approximate likelihoods, whereas the second builds a likelihood based on a "skeleton", a sample of plausible missing paths compatible with the observed data. Both methods have practical difficulties. To overcome these issues we propose a novel importance sampler that combines the best of both methods. Using data simulated from various models in both discrete and continuous time settings, we demonstrate the performance of our method. We motivate this research with an application from finance.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education 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.