Michael Robbins

Photo of Michael Robbins
Statistician
Pittsburgh Office

Education

Ph.D. in mathematical sciences, Clemson University; M.S. in mathematical sciences, Clemson University; B.S. in mathematics, economics, Duke University

Overview

Michael Robbins is a statistician at the RAND Corporation. He has a diverse background that includes thorough training in applied and theoretical statistics. His methodological interests include survey design and methodologies, causal inference, change-point analysis, missing data analysis, time series analysis, multivariate analysis, and stochastic processes. Robbins is also interested in the application of statistics to a variety of topical areas, including economics, environment, health care, education, and defense policy. Prior to joining RAND, he worked as an assistant professor at the University of Missouri, Columbia, and as a postdoctoral fellow with the National Institute of Statistical Sciences. Robbins earned his M.S. and Ph.D. in Mathematical Sciences from Clemson University.

Recent Projects

  • A Multi-Phase Survey Strategy for Obtaining Representativeness of Big Data
  • Data Fusion for Predicting Long-Term Program Impacts
  • An Innovative Approach for Measuring Performance of Border Security Programs
  • Expand ATP and ASLP Panels in Selected States

Selected Publications

M. W. Robbins, "A Fully Flexible Changepoint Test for Regression Models with Stationary Errors.," Statistica Sinica, 2019 (forthcoming)

M. W. Robbins; J. Saunders; B. Kilmer, "A Framework for Synthetic Control Methods with High Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention," Journal of the American Statistical Association, 112, 2017

M. W. Robbins, C. M. Gallagher, R. B. Lund, "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, 111, 2016

M. W. Robbins and T. J. Fisher, "Cross-Correlation Matrices for Tests of Independence and Causality between Two Multivariate Time Series," Journal of Business and Economic Statistics, 33, 2015

M. W. Robbins, "The Utility of Nonparametric Transformations for Imputation of Survey Data," Journal of Official Statistics, 30, 2014

M. W. Robbins, S. K. Ghosh, and J. D. Habiger, "Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey," Journal of the American Statistical Association, 108, 2013

M. W. Robbins, R. B. Lund, C. M. Gallagher, Q. Lu, "Changepoints in the North Atlantic tropical cyclone record," Journal of the American Statistical Association, 106, 2011

M. W. Robbins, C. M. Gallagher, R. Lund, A. Aue, "Mean shift testing in correlated data," Journal of Time Series Analysis, 32, 2011

Publications