Abstract Details
Activity Number:
|
74
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #311491
|
View Presentation
|
Title:
|
Nonparametric Bayesian Particle Learning with Applications to Income Volatility
|
Author(s):
|
Julie Novak*+ and Tung Phan and Shane Jensen
|
Companies:
|
Wharton School and Wharton School and University of Pennsylvania
|
Keywords:
|
Bayesian Nonparametric ;
Income Volatility ;
Dynamic Linear Modeling ;
Particle Learning ;
Heirarchical Dirichlet Process
|
Abstract:
|
We propose a new method for estimating hidden states and parameters of state-space models. This approach combines particle learning and hierarchical Dirichlet process (HDP) to provide an efficient way to analyze hierarchical data. We compare our method to others, such as combining Kalman Filters and HDPs, and analyze model performance. We then apply our method to labor incomes from the core sample of the Panel Study of Income Dynamics (PSID) data set to demonstrate the importance of our new method.
|
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.