Robust Small Area Estimation
Julie Gershunskaya1, Partha Lahiri2
1U.S. Bureau of Labor Statistics, Washington, DC, United States; 2Joint Program in Survey Methodology, University of Maryland, College Park, MD, United States

Different methods have been proposed in the small area estimation literature to deal with influential observations. In this paper, we develop a robust estimation technique that is reasonably resistant to influential observations and a parametric bootstrap method to estimate the mean squared error of the proposed robust estimator. Using a Monte Carlo simulation study, we compare our proposed method with a few recently proposed robust small area estimators. Empirical evaluation of the estimators is performed using the population data from administrative file.

Keywords: Influential observations; Mixture model; Outlier; Empirical best prediction

Biography: Partha Lahiri is a Professor of the Joint Program in Survey Methodology (JPSM) at the University of Maryland, College Park, and an Adjunct Research Professor of the Institute of Social Research, University of Michigan, Ann Arbor. Dr. Lahiri has been honored by being made a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.