A special feature of Probability and Statistics Day at UMBC 2015 is that the conference, including the workshop, is open to all statistics graduate students
from UMBC and local universites free of charge; however, REGISTRATION IS REQUIRED! The deadline to register is Friday,
April 3, 2015. // REGISTER NOW
For more information, contact any member of the organizing committee:
Bimal Sinha
Conference Chair
443.538.3012
Kofi Adragni
410.455.2406
Yvonne Huang
410.455.2422
Yaakov Malinovsky
410.455.2968
Thomas Mathew
410.455.2418
Nagaraj Neerchal
410.455.2437
DoHwan Park
410.455.2408
Junyong Park
410.455.2407
Anindya Roy
410.455.2435
Elizabeth Stanwyck
410.455.5731
Participant Information
April Albertine
Paper: Comparison of several types of confidence intervals based on multiply imputed synthetic data under a normal model
The release of synthetic data sets is one approach for protecting confidentiality when the release of the original survey microdata is impossible due to privacy concerns. The goal is to preserve important summary features (thereby allowing outside researchers the opportunity for analysis) while disguising individual distinguishing responses that could be used to identify the subjects themselves. A single synthetic data set can be made public (single imputation), or even multiple synthetic data sets generated from the same raw data (multiple imputation). We consider multiply imputed synthetic data where each set is imputed via posterior predictive sampling, assuming that the original data is normally distributed. We evaluate several different methods of combining the summary statistics of the synthetic data sets in order to do inference on the parameters of the underlying normal model. The different methods of combining the synthetic data sets are compared according to the expected length of confidence intervals for the two parameters of the normal model. We present an application using data on household income from the United States Current Population Survey.
