Statistics Seminar (2019-03)

Topic: Statistical Analysis of Noise Multiplied Data Using Multiple Imputation

Speaker: Bimal Sinha, University of Maryland, Baltimore County

Time: Thursday, Mar 21, 14:00-15:00

Place: Room 217, Guanghua Building 2

Abstract:

A statistical analysis of data that have been multiplied by randomly drawn noise variables in order to protect the confidentiality of individual values has recently drawn some attention. If the distribution generating the noise variables has low to moderate variance, then noise multiplied data have been shown to yield accurate inferences in several typical parametric models under a formal likelihood based analysis. However, the likelihood based analysis is generally complicated due to the non-standard and often complex nature of the distribution of the noise perturbed sample even when the parent distribution is simple. This complexity places a burden on data users who must either develop the required statistical methods or implement the methods if already available or have access to specialized software perhaps yet to be developed. In this paper we propose an alternate analysis of noise multiplied data based on multiple imputation. Some advantages of this approach are that (1) the data user can analyze the released data as if it were never perturbed, and (2) the distribution of the noise variables does not need to be disclosed to the data user.

Introduction:

Professor Sinha is the Founder of the Statistics Graduate Program at University of Maryland, Baltimore County (UMBC). A 1973 Ph.D. in statistics from the University of Calcutta/India, Professor Sinha is an ex-faculty of the Indian Statistical Institute and the University of Pittsburgh. A Professor of Statistics at UMBC since 1985, Professor Sinha's research activities span topics in theoretical and applied statistics, including multivariate analysis, linear models, ranked set sampling, environmental statistics, statistical meta-analysis, and data analysis under confidentiality protection. He has co-edited several volumes, and coauthored four books (John Wiley, Springer, Academic). He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. His research has been funded by the US Environmental Protection Agency for about twenty years.

Your participation is warmly welcomed!