Mike Higgins

003 Dickens Hall
Department of Statistics
Kansas State University


mikehiggins AT ksu DOT edu

Hi! I'm Mike Higgins. I am an Assistant Professor in the Department of Statistics at Kansas State University.

My research involves developing statistical methods and theory motivated by problems in the Social Sciences. Broad statistical interests include Causal Inference, Experimental Design, and Nonparametric Statistics. My work has been applied to problems in election auditing and redistricting. I am especially interested in the use of integer programming methods to solve statistical problems.

I received my PhD in Statistics from the University of California at Berkeley. I was co-advised by Jasjeet Sekhon and Deborah Nolan.

A recently updated copy of my CV can be found here.


Papers

Higgins, M.J., F. Savje, and, J.S. Sekhon, 2015. "Improving massive experiments with threshold blocking," PNAS, Forthcoming.

Higgins, M.J., R.L. Rivest, and P.B. Stark, 2011. "Sharper p-values for Stratified Post-Election Audits," Statistics, Politics, and Policy: Vol. 2: Iss. 1, Article 7.


Selected Works in Progress

"The Benefits of Probability Proportional to Size Sampling in Cluster-Randomized Experiments."

"A New Automatic Redistricting Simulator using Markov Chain Monte Carlo." With Ben Fifield, Kosuke Imai, and Alex Tarr.

"Blocking Estimators and Inference Under the Neyman-Rubin Model." With Fredrik Savje and Jasjeet Sekhon.


Selected Talks and Posters

"The Benefits of Probability Proportional to Size Sampling in Cluster Randomized Experiments." Annual Meeting of the American Political Science Association. 2014.

"A new automated redistricting algorithm using MCMC." Annual Meeting of the Society for Political Methodology. With Ben Fifield and Kosuke Imai. 2014.

"The Benefits of Probability Proportional to Size Sampling in Cluster Randomized Experiments." Poster presentation. Annual Meeting of the Society for Political Methodology. 2014.

"Improving Experiments by Optimal Blocking: Minimizing the Maximum Within-block Distance." Kansas State University Statistics Seminar. 2013.

"Improving Experiments by Optimal Blocking: Minimizing the Maximum Within-block Distance." Joint Statistical Meetings. Montreal, QC. August 5, 2013.

"Improving Experiments by Optimal Blocking: Minimizing the Maximum Within-block Distance." Annual Meeting of the Society for Political Methodology. With Jasjeet Sekhon. Charlottesville, VA. July 18, 2013.

"Sharper p-values for Stratified Election Audits." Poster presentation. Annual Meeting of the Society for Political Methodology. Charlottesville, VA. July 18, 2013.

"Optimal Blocking by Minimizing the Maximum Intra-block Dissimilarity." U.S. Census Bureau, Center for Statistical Research & Methodology. Suitland, MD. March 5, 2013.

"Exact Inference using Stratified Random Samples and the 0-1 Knapsack Problem". Joint Statistical Meetings. Miami, FL. August 4, 2011.


R Packages

elec.strat: Package for election audits using stratified random samples. Co-authored with Hua Yang.


Courses Taught

STAT 706: Basic Elements of Statistical Theory Fall 2015
STAT 950: Introduction to Causal Inference Spring 2016



© Mike Higgins, 2013