Naijun Sha

Office: Bell Hall 203
Phone: 915-747-6844
Fax: 915-747-6502
E-mail: nsha@utep.edu

Department of Mathematical Sciences
University of Texas at El Paso
500 W University Ave
El Paso, TX 79968-0514

Education

Research Interests

Classification and Clustering, Variable Selection Technique, Reliability, Bayesian Approach, Bioinformatics.

Courses (Fall 2015)

Honors and Awards

2005-2012, Elected Member, Marquis Who's Who in America.
Jan. 2004, Elected Member, Academic Keys Who's Who in Sciences Higher Education (WWSHE).
Apr. 2001, Phi Kappa Phi, Texas A&M University.

Select Publications

Wang, R., Sha, N., Gu, B. and Xu, X. (2015). Parameter inference in a hybrid system with mask data. IEEE Transactions on Reliability, 64(2), 636-644.

Sha, N. and Pan, R. (2014). Bayesian analysis for step-stress accelerated life testing using weibull proportional hazard model. Statistical Papers, 55, 715-726.

Wang, R., Sha, N., Gu, B. and Xu, X. (2012). Comparison analysis of efficiency for step-down and step-up stress accelerated life testing. IEEE Transactions on Reliability, 61(2), 590-603.

Savistsky, T., Vannucci, M. and Sha, N. (2011). Variable selection for nonparametric Gaussian process priors: models and computational strategies. Statistical Science, 26(1), 130-149.

Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian variable selection for the analysis of microarray data with consored outcomes. Bioinformatics, 22(18), 2262-2268.

Tadesse, M., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of American Statistical Association, 100, 602-617.

Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J., Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon, M., Buckley, C. and Falciani, F. (2004). Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60(3), 812-819.

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