Alan M. Polansky, Ph.D.

Associate Professor

Alan Polansky joined NIU in 1995.

He earned his Ph.D. from Southern Methodist University, his M.S. from the University of Texas at San Antonio and his B.S. from the University of Texas at San Antonio.

Research

Polansky's research lies in nonparametric statistics, bootstrap methods and statistical inference, with applications to quality control, network analysis and stochastic processes. His contributions include kernel-smoothed bootstrap confidence intervals, observed confidence levels for simultaneous inference, nonparametric process capability analysis and change-point detection in Markov chains. He is the author of two CRC/Chapman and Hall books — Observed Confidence Levels: Theory and Application and Introduction to Statistical Limit Theory — has published in the Journal of the Royal Statistical Society, Technometrics and the Journal of Multivariate Analysis, and co-founded the ASA Section on Nonparametric Statistics.

Education

• Ph.D. Southern Methodist University
• M.S. University of Texas at San Antonio
• B.S. University of Texas at San Antonio

Selected Publications

Akakpo, R. M., Xia, M., and Polansky, A. M. (2019). Frequentist inference in insurance ratemaking models adjusting for misrepresentation. ASTIN Bulletin - The Journal of the International Actuarial Association.

Polansky, A. M., and Ghosh, S. (2016). Using observed confidence levels to perform principal component analyses. Communications in Statistics Series A: Theory and Methods, 45, 3596-3611.

Ghosh, S., and Polansky, A. M. (2016). New bootstrap confidence intervals for means of positively skewed distributions. Communications in Statistics Series A: Theory and Methods, 45, 6915-6927.

Polansky, A. M., and Maple, A. (2014). Using Bayesian models to assess the capability of a manufacturing process in the presence of unobserved assignable causes. Quality Technology and Quantitative Management, 13, 139-164.

Ghosh, S., and Polansky, A. M. (2014). Smoothed and iterated bootstrap confidence regions for parameter vectors. Journal of Multivariate Analysis, 132, 171-182.

Polansky, A. M. (2011). Introduction to statistical limit theory. Boca Raton, FL: Chapman and Hall/CRC Press.

Polansky, A. M. (2007). Observed confidence levels: Theory and applications. Boca Raton, FL: Chapman and Hall/CRC Press.

Polansky, A. M. (2007). Detecting change-points in Markov chains. Computational Statistics and Data Analysis, 51, 6013-6026.

Polansky, A. M. (2003). Selecting the best treatment in designed experiments. Statistics in Medicine, 22, 3461-3471.

Polansky, A. M. (2000). A smooth nonparametric approach to multivariate process capability. Technometrics, 43, 199-211.

Polansky, A. M., and Schucany, W. R. (1997). Kernel smoothing to improve bootstrap confidence intervals. Journal of the Royal Statistical Society, Series B, 59, 821-838.

Honors Faculty Fellow (2022-2023)

As an Honors Faculty Fellow, Professor Polansky will teach a seminar on Data and Social Justice in fall 2022 in the University Honors Program. The Honors Faculty Fellowship program identifies faculty eager to teach innovative, exciting seminars of interest to highly-motivated students from across the university.