American Journal of Mathematics and Statistics

American Journal of Mathematics and Statistics is to publish well-written original research articles and studies describing the latest research and developments in the field of mathematics and statistics. This is a broad-based journal covering all branches of mathematics, statistics and interdisciplinary research.

Alan Polansky

Editorial Board Member of American Journal of Mathematics and Statistics

Associate Professor, Northern Illinois University, USA

Research Areas

Nonparametric Statistics, Smoothing Methods, Nonparametric Bayesian Statistics, Statistical Quality Control, Confidence Measures

Education

1995Ph.DSouthern Methodist University, Dallas, Texas
1991M.ScThe University of Texas at San Antonio, San Antonio, Texas
1990B.ScThe University of Texas at San Antonio, San Antonio, Texas

Experience

1995-presentAssociate Professor, Northern Illinois University, Division of Statistics, De Kalb, IL
1994-1995Instructor, Southern Methodist University, Department of Statistical Science, Dallas, TX
1993Data Manager and Analyst, Texas Transportation Institute, The Texas A&M University System, Dallas, TX
1993Instructor, Cuplex Incorporated; Garland, TX
1992Instructor, The University of Texas at San Antonio, Division of Mathematics, Computer Science and Statistics, San Antonio, TX
1992-1993Data Manager, The University of Texas Health Science Center at San Antonio, Department of Community Dentistry, San Antonio, TX

Academic Achievement

National Science Foundation, SCREMS Grant, Number DMS 970 7721. Dates: 6/1/1997-6/1/1998. Topic: Performance of Bootstrap Confidence Intervals and Tests. Principal Investigator: Dr. Mohsen Pourahmadi
Graduate School, Internal Summer Grant. Northern Illinois University. Dates: 5/16/2000-6/15/2000. Topic: Data Based Bandwidth Selection for the Smoothed Bootstrap
Graduate School, Internal Summer Grant. Northern Illinois University. Dates: 7/16/1996-8/15/1996. Topic: Tests for trends in environmental compliance

Membership

American Society for Quality (Senior Member)
American Statistical Association
The International Association for Statistical Computing
Co-Founder and Publications Officer for the Section on Nonparametric Statistics of the American Statistical Association

Publications: Journals

[1]  Chou, Y.-M. and Polansky, A. M. (1993). Power of tests of some process capability indices. Communications in Statistics, Series B: Simulation and Computation, 22, 523–544.
[2]  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.
[3]  Polansky, A. M. (1997). Inexact control variates for the iterated bootstrap. Journal of Statistical Computation and Simulation, 59, 83–99.
[4]  Guerra, R., Polansky, A. M. and Schucany, W. R. (1997). Smoothed bootstrap confidence intervals with discrete data. Computational Statistics and Data Analysis, 26, 163–176.
[5]  Chou, Y.-M., Polansky, A. M., and Mason, R. L. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30, 133–141.
[6]  Polansky, A. M. (1998). A smooth nonparametric approach to process capability. Quality and Reliability Engineering International, 14, 43–48.
[7]  Polansky, A. M., Chou, Y.-M. and Mason R. L. (1998). Estimating process capability indices for truncated distributions. Quality Engineering, 11, 257-265.
[8]  Polansky, A. M. (1999). Upper bounds on the true coverage of bootstrap percentile type confidence intervals. American Statistician, 53, 362–369.
[9]  Polansky, A. M., Chou, Y.-M., and Mason, R. L. (1999). An algorithm for fitting Johnson transformations to non-normal data. Journal of Quality Technology, 31, 345–350.
[10]  Polansky, A. M. (2000). An algorithm for computing a smooth nonparametric process capability estimate. Journal of Quality Technology, 32, 284–289.
[11]  Polansky, A. M. (2000). Stabilizing bootstrap-t confidence intervals for small samples. Canadian Journal of Statistics, 28, 501–516.
[12]  Polansky, A. M. and Baker E. R. (2000). Multistage plug-in bandwidth selection for kernel distribution function estimates. Journal of Statistical Computation and Simulation, 65, 63–80.
[13]  Polansky, A. M. (2000). A smooth nonparametric approach to multivariate process capability. Technometrics, 43, 199–211.
[14]  Polansky, A. M. (2000). Bandwidth selection for the smoothed bootstrap percentile method. Computational Statistics and Data Analysis, 36, 333–349.
[15]  Polansky, A. M. and Check, C. E. (2001). Tests for trends in environmental compliance. Journal of Agricultural, Biological and Environmental Statistics, 7, 452–468.
[16]  Polansky, A. M. and Kirmani, S. N. U. A. (2002). Quantifying the capability of industrial processes. Handbook of Statistics: Statistics in Industry, Vol 22, 625–656.
[17]  Polansky, A. M. (2003). Supplier selection based on bootstrap confidence regions of process capability indices. Reliability, Quality and Safety Engineering, 10, 1–14.
[18]  Polansky, A. M. (2003). Selecting the best treatment in designed experiments. Statistics in Medicine, 22, 3461–3471.
[19]  Polansky, A. M. (2005). A general framework for constructing control charts. Quality and Reliability Engineering International, 21, 633–653.
[20]  Polansky, A. M. (2005). Nonparametric estimation of distribution functions of non-standard mixtures. Communications in Statistics-Theory and Methods, 34, 1711–1724.
[21]  Polansky, A. M. (2006). Permutation methods for comparing process capabilities. Journal of Quality Technology, 38, 254–266.
[22]  Polansky, A. M. (2007). Detecting change-points in Markov chains. Computational Statistics and Data Analysis, 51, 6013–6026.
[23]  Polansky, A. M. (2007). Nonparametric process capability indices. Encyclopedia of Statistics in Quality and Reliability, Volume III, 1462–1466.
[24]  Chou, Y.-M. and Polansky, A. M. (2007). Process capability indices for non-normal distributions. Encyclopedia of Statistics in Quality and Reliability, Volume III, 1459–1462.
[25]  Polansky, A. M. (2007). Sampling from virtual populations. Encyclopedia of Statistics in Quality and Reliability, Volume IV, 1715–1719.
[26]  Kirmani, S. N. U. A. and Polansky, A. M. (2009). Multivariate process capability via Lowner ordering. Linear Algebra and Its Applications, 430, 2681–2689.
[27]  Frobish, D., Ebrahimi, N. and Polansky, A. M. (2009). Parametric estimation of change-points for panel count data in recurrent events models. Journal of Statistics and Applications, to appear.
[28]  Polansky, A. M. (2009). Using the smoothed bootstrap for statistical inference for Markov chains. The Pakistani Journal of Statistics, 25, 553-570.
[29]  Polansky, A. M. (2010). Ordered inference using observed confidence levels. Computational Statistics and Data Analysis, 54, 233-244.

Publications: Conferences/Workshops/Symposiums

[1]  Chou, Y.-M. and Polansky, A. M. (1996). Fitting SPC data using a sample quantile ratio. ASA Proceedings of the Section on Quality and Productivity, 9-16. American Statistical Association, Alexandria, Virginia.

Publications: Books/Book Chapters

[1]  Polansky, A. M. (2007). Observed Confidence Levels: Theory and Application. CRC/Chapman and Hall.
[2]  Polansky, A. M. (2010). Introduction to Statistical Limit Theory. CRC/Chapman and Hall.