The Uses of Statistics and Data-Analytics at a Federal Reserve Bank
Dr. Kartik B. Athreya
Federal Reserve Bank of Richmond
Friday, March 6, 2020
11:00 am - 12:00 pm
Grace Harris Hall, Room 4155
Dr. Athreya will talk about the uses of statistics, and career pathways, at the Richmond Fed and in the Federal Reserve System. He also plans to spend some time speaking on the relation between statistics and (macro)economics. That latter part is not immediately about career opportunities, but rather to give some perspective on how the use of statistics is complicated by the (highly) non-experimental data of economics. That is to help show stats students how parts of statistics (like econometrics and macroeconomic models) have evolved to deal with very specific problems in social science settings.
Dr. Kartik B. Athreya is the Executive Vice President and Director of Research at the Federal Reserve Bank of Richmond. Athreya's work has been published in a variety of academic journals, including the Journal of Monetary Economics, American Economic Journal: Macroeconomics, and International Economic Review. He is also an associate editor at the Journal of Economic Dynamics and Control. In recent years, Athreya has taught a doctoral course in macroeconomics at the University of Virginia and authored a book entitled Big Ideas in Macroeconomics (2013, MIT Press). He earned his doctorate from the University of Iowa in 2000.
Measuring Sensitivity to Nonignorable Incompleteness
Dr. Daniel F. Heitjan
Southern Methodist University and UT Southwestern Medical Center
Friday, February 21, 2020
12:00 pm - 12:50 pm
Academic Learning Commons (MCALC), Room 1104
Statisticians have long recognized the potential biasing effects of nonignorable missing data mechanisms. For example, if larger observations are more likely to be missing or censored, then standard estimates such as the sample mean of the observed data or the Kaplan-Meier curve are invalid. Unfortunately, methods that attempt to estimate or test the degree of nonignorability are unsatisfactory. My idea is to embed the reference ignorable model in a nonignorable model in which I define a nonignorability parameter to represent the degree of departure from missing at random. I then conduct a sensitivity analysis to evaluate the dependence of the MLE of the parameter of interest varies with the degree of nonignorability. If it takes a large value of the nonignorability parameter to substantially affect the estimated parameter of interest, we judge the standard analysis to be insensitive. In this talk, I describe an approach to such a sensitivity analysis based on the index of local sensitivity to nonignorability (ISNI) statistic. An R package is available to conduct this analysis for the univariate GLM and a range of models for clustered or longitudinal data. I will demonstrate applications in live-data examples.
Dr. Daniel F. Heitjan is Professor and Chair of Statistical Science at Southern Methodist University and Professor of Population & Data Sciences at UT Southwestern Medical Center. A native of Detroit, he earned a BSc in Mathematics (1981), an MSc in Statistics (1984), and a PhD in Statistics (1985) from the University of Chicago. He served on the faculties of UCLA (1985-1988), Penn State (1988-1995), Columbia University (1995-2002), and the University of Pennsylvania (2002-2014) before moving to Texas in 2014. Dr. Heitjan has over 200 publications in the literature of medicine and statistics, and is an elected Fellow of the American Statistical Association (1997), the Institute of Mathematical Statistics (2012), and the Society for Clinical Trials (2017). His research interests include clinical trial design, Bayesian methods, the theory of inference with incomplete data, and statistical methods in health economics, pharmacogenomics, and smoking cessation research.
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