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.
About the Speaker:

Professor Sinha is the Founder of the Statistics Graduate Program at University of Maryland, Baltimore County (UMBC). A 1973 PhD 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 coedited 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.