Abstract Details
Activity Number:
|
462
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Government Statistics Section
|
Abstract #311902
|
|
Title:
|
Likelihood-Based Finite Sample Inference for Synthetic Data from a Multiple Linear Regression Model
|
Author(s):
|
Martin Klein*+
|
Companies:
|
U.S. Census Bureau
|
Keywords:
|
Maximum likelihood estimator ;
Pivot ;
Plug-in sampling ;
Posterior predictive sampling ;
Statistical disclosure control
|
Abstract:
|
Likelihood-based finite sample inference based on synthetic data is developed in this paper under a multiple linear regression model. We consider two distinct synthetic data generation scenarios, one based on posterior predictive sampling, and the other based on plug-in sampling where unknown parameters are set equal to the observed value of their point estimators. We demonstrate that valid inference can be drawn in both scenarios, even for a singly imputed synthetic dataset; and we show that plug-in sampling will generally lead to more efficient inference than posterior predictive sampling. We discuss the usual combination rules for drawing inference under multiple synthetic datasets in the context of likelihood-based data analysis.
|
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.