IWEICS2010 IMST2007 Teaching  

Bin Wang

Position Held
Associate Professor of Statistics 2009--present  Mathematics and Statistics Department, University of South Alabama
Assistant Professor of Statistics 2003 -- 2009  Mathematics and Statistics Department, University of South Alabama

Education
Ph.D. of Statistics 2003  Case Western Reserve University
M.S. 1999  University of Science and Technology of China
B.S. 1993  University of Science and Technology of China

Honors and Memberships
Invited Guest Editor of AJMMS special volume for IMST 2007 (Shanghai, China). May 2007 -- present. (final package, restricted access.)
Life member of Institute of Mathematical Statistics; Committee member of ASA/SRCOS (01/01/2006-12/31/2008); Member of ASA (2000-2008); Member of ENAR (2000-2005).

Research Interests
Biased Sampling; Semi-/Non-parametric Method; Density Estimation; Generalized Model Fitting and Bootstrapping; Microarray Data Analysis; Bioinformatics; Measurement Errors; Fractals.

Selected Publications
  1. Wang, B., Wang, X-F. and Xi, Y. (2011) "Normalizing bead-based microRNA expression data: a measurement error model-based approach", Bioinformatics, 27(11), 1506-1512.
  2. Wang, X-F. and Wang, B. (2011) Deconvolution Estimation in Measurement Error Models: The R Package decon, Journal of Statistical Software, 39(10), 1-24. link
  3. Wang, B., Howell, P., Bruheim, S. Ju, J, Owen, L.B., Fodstad, O. and Xi, Y. (2011). Systematic Evaluation of Three microRNA Profiling Platforms: Microarray, Beads Array, and Quantitative Real-Time PCR Array, PLoS ONE 6(2): e17167. doi:10.1371/journal.pone.0017167
  4. Wang, B., Wang X., Howell, P., Qian, X., Huang, K., Riker, A.I., Ju, J. and Xi, Y. (2010). A personalized microRNA microarray normalization method using a logistic regression model, Bioinformatics, 26(2): 228-234. link
  5. Wang, X.F., Fan, Z. and Wang, B. (2010). Estimating smooth distribution function in the presence of heteroscedastic measurement errors. Computational Statistics and Data Analysis, 54, 25-36.
  6. Wang, B., Mishra, S.N., Mulekar, M., Mishra, N.S., Huang, K., (2010). Comparison of bootstrap and generalized bootstrap methods for estimating high quantiles, Journal of Statistical Planning and Inferences, 140. 2926-2935. DOI: 10.1016/j.jspi.2010.03.016.
  7. Wang, B., Mishra, S.N., Mulekar, M., Mishra, N.S., Huang, K., (2010). Generalized Bootstrap Confidence Intervals for High Quantiles, In: Karian ZA, Dudewicz, EJ eds. The Handbook on Fitting Statistical Distributions with R. CRC Press. 2010: 877-913.
  8. Zhang, XM., Hitt, R. Wang, B. and Ding, J. (2008). Sierpi\'{n}ski Pedal Triangle. Fractals. 16(2): 141-150.
  9. Wang, B. and Sun, J. (2008). Inferences from Biased Samples with a Memory Effect. Journal of Statistical Planning and Inferences, Volume 139, Issue 2: 441-453.
  10. Sun, J. and Wang, B. Sieve Estimates for Biased Survival Data, IMS Lecture Notes-Monograph Series: Recent Development in Nonparametric Inference and Probability: Festschrift for Michael Woodroofe 2006. 50, 127-143.
Complete list of published papers and on-going manuscripts

Software and R Packages Developed
  1. CRAN R package: decon: a package to estimate distribution function and density function based on data contaminated with homoscedastic or heteroscedastic measurement errors under the classic measurement error model (Joint with X-F. Wang, a paper has been published in CSDA and the package has been published in JSS).
  2. CRAN R package: bstats: a package to collect some basic statistical functions/algorithms. This package is specifically designed for ST 335, ST475/575 classes I teach in University of South Alabama.
  3. CRAN R package: spt: this package is built to collect functions/algorithms developed for constructing Sierpinski Triangles and Sierpinski Pedal Triangles.
  4. CRAN R package: gb: In this package, we collected algorithms to fit generalized lambda distributions (GLDs) to data using three moment-matching method: method of mementos, method of percentiles, and method of L-moments. In addition, an algorithm to fit a generalized beta distribution (GBD or EGLD: extended generalized lambda distribution) using an maximum likelihood approach is developed. A generalized bootstrapping algorithm is implemented as well.
  5. CRAN R package: bda: a package for rounded data analysis. Algorithms include a parametric density estimator, a nonparamatric bootstrap kernel density estimator, an expectation-maximization algorithm to fit finite mixture models. In addition, a smoothed kernel estimator and a histospline density estimator are collected as well.
    • Site for supplementary materials for CSDA paper: "A Bootstrap Kernel Density Estimator for Data With Rounding Errors", by Wang, B. and Wertelecki, W., (2012).
  6. Bioinformatics: MicroRNA microarray data normalization using a quantitative real-time PCR based logistic regression model
  7. Bioinformatics: Normalizing bead-based microRNA expression data: a measurement error model-based approach
  8. Computing Probabilities of a Trinomial Distribution on-line: link (written in PHP to meet the needs of Dr. Rainosek).

© Bin WANG 2011. Last updated: June 2 2011 09:44:31.
Contact: Mathematics and Statistics Department, 411 University BLVD N, Room 325, Mobile AL 36688
Email: bwang at jaguar1 dot usouthal dot edu; Phone: 1-251-460-6264 ext 2626 (Office)