Mathematics and Statistics Department, University of South Alabama

Associate Professor of Statistics

2009 - 2013

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

Wang, B., Zhang, S-G., Wang, X-F., Tan, M. and Xi, Y. (2012) "Testing for differentially-expressed microRNAs with errors-in-variables nonparametric regression", PLoS ONE 7(5): e37537. doi:10.1371/journal.pone.0037537.

Wang, B. and Wertelecki, W. (2012) "Density Estimation for Data With Rounding Errors", Computational Statistics and Data Analysis, (in press). doi: 10.1016/j.csda.2012.02.016.

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.

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

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

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

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.

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.

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.

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.

Wang, B. and Wang, X-F. (2007) Bandwidth Selection for Weighted Kernel Density Estimation (unpublished manuscript, pdf).

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).

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.

CRAN R package: spt: this package is built to collect functions/algorithms developed for constructing Sierpinski Triangles and Sierpinski Pedal Triangles.

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.

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).