Dr. Partha Lahiri, Joint Program in Survey Methodology, University of Maryland, College Park The demand for various socio-economic and health statistics for small geographical areas is steadily increasing at a time when survey agencies are looking for ways to reduce costs to meet fixed budgetary requirements. In the current survey environment, the application of standard sample survey methods for small areas, which require large sample, is generally not feasible from the cost consideration. One of the key factors that lead to the success of small area methodology, which typically uses implicit or explicit models to combine survey and administrative data sources, is the availability of strong auxiliary variables. The accessibility of big data from different sources is now bringing new opportunities for statisticians to develop innovative small area methods. In this talk, I will first give an overview of the current state of small area research and then discuss the potential for the use of big data in producing reliable local area statistics.
Dr. Partha Lahiri is Professor of Survey Methodology and Statistics at the University of Maryland, College Park. Prior to coming to Maryland, Dr. Lahiri was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His research interests include Bayesian statistics, record linkage and small-area estimation. Dr. Lahiri has served on a number of advisory committees, including the U.S. Census Advisory committee and U.S. National Academy panel. Over the years Dr. Lahiri advised various local and international organizations such as the United Nations Development Program, World Bank, and the Gallup Organization. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.
January 29, 2015