DuSable Hall 361D
Professor Polansky grew up in San Antonio, Texas, where his father worked in construction as a carpenter and foreman, and his mother worked as a seamstress. He earned his BS and MS in mathematics from the University of Texas at San Antonio and wrote his master's thesis under the direction of Professor Youn-Min Chou in the area of statistical quality control. After teaching at the university for a year, and having a brief career in the insurance industry, Professor Polansky entered Southern Methodist University in Dallas to work on his PhD in Statistics. He earned this degree in 1995 under the direction of Professor William Schucany. He worked with the smoothed bootstrap and nonparametric confidence intrvals.
Most of Professor Polansky's work has centered around two modern nonparametric methods: the bootstrap and smoothing methods. Nonparametric statistical methods attempt to find valid methods for statistical inference without making many assumptions about the underlying population. The bootstrap is a general methodology developed by Bradley Efron in 1977 that replaces analytical calculations with computer basd simulations. Smoothing methods seek to replace parametric assumptions like linearity in regression with less restrictive assumptions such as the regression curve being differntiable.
He has developed an approach to statistical problems that are usually solved using multiple comparison techniques. These methods involve a measure of confidence Professor Polansky developed in his 2007 book Observed Confidence Levels: Theory and Application, published by CRC/Chapman and Hall. He has devoted much of his recent research to applying this measure of confidence to many common statistical problems like restricted inference and principal components.
His latest research has concentrated on statistical approaches to analyzing data arising from network graphs and on applications of hierarchical and nonparametric Bayesian methods in reliability and industrial statistics applications.
Here are some of Professor Polansky's recent publications:
Polansky, A. M. (2011). Introduction to Statistical Limit Theory. CRC/Chapman and Hall.
Kirmani, S. N. U. A. and Polansky, A. M. (2009). Multivariate process capability via Lowner ordering. Linear Algebra and Its Applications, to appear.
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.
Polansky, A. M. (2007). Observed Confidence Levels: Theory and Applications. Chapman and Hall/CRC Press, Boca Raton, FL.
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 desgned experiments. Statistics in Medice, 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.
Professor Polansky's hobbies include music, railfanning, and model railroading.
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