American Statistical Association
Surveys are an important source of data in many social, health and environmental sciences. When analyzing datasets that were obtained through surveys, failure to take the design aspects into account can lead to invalid estimation and inference, even when the mode of inference is model-based. We begin by describing some of the issues associated with informativeness in analytic inference, and examine the differences between model-based and design-based modes of interference. We then describe a number of new results and approaches in testing and adjusting for design effects, using a combination of parametric and nonparametric tools.
Originally, from Belgium, I have an MBA from the University of Chicago. I obtained my PhD in Operations Research at Cornell University. After 12 years in the Department of Statistics at Iowa State University, I moved to the Department of Statistics at Colorado State University in 2007, and I have been the department Chair since January 2011. I am a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. I am currently the Chair of the ASA Nonparametric Section, and I am a member of the Executive Committee of the Institute of Mathematical Statistics. My research interests are in survey statistics, nonparametric statistics and environmental statistics.
|Date:||Thursday, November 3, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York