报告会

Comparison Analysis of Efficiency for Step-Down and Step-Up Stress Accelerated Life Testing
Naijun Sha
Department of Mathematical Sciences
University of Texas at El Paso
Abstract: We compare the practical efficiency of step-down and step-up stress accelerated life tests based on four different criteria through theoretical studies, and intensive Monte-Carlo simulations. We consider Weibull, and lognormal distributions for the lifetime of products at each stress level; and deal with type-I, and type-II censoring schemes. Through theoretical and simulation studies, we demonstrate that under a type-I censoring scheme, the step-down test plan results in a shorter mean
failure time of testing units than that of the step-up stress accelerated life test. With type-II censoring, the step-down test is also preferred for products with long lifetimes, while the step-up procedure works better applied to units with early and occasional failures.
CURRICULUM VITAE
NAIJUN SHA
Department of Mathematical Sciences
University of Texas at El Paso
El Paso, TX 79968-0514
EDUCATION
2002, Ph.D., Statistics, Texas A&M University.
1997, M.S., Statistics, The University of Texas at El Paso.
1985, B.S., Mathematics, Fudan University, China.
RESEARCH INTERESTS
Classification/Clustering, Bayesian Approach, Variable Selection, Bioinformatics.
ACADEMIC APPOINTMENTS
2008- Associate Professor, Department of Mathematical Sciences
University of Texas at El Paso, El Paso, TX.
2002-2008 Assistant Professor, Department of Mathematical Sciences
University of Texas at El Paso, El Paso, TX.
MEMBERSHIPS
American Statistical Association (ASA), Institute of Mathematical Statistics (IMS)
SELECTED PUBLICATIONS
1. Wang, R., Xu, X., Pan, R., Sha, N. (2012). On parameter inference for step-stress accelerated life test with geometric distribution. Communications in Statistics – Theory and Methods, 41(10), 1796–1812.
2. 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
3. Jeong, J., Vannucci, M., Do, K. A., Broom, B., Kim, S., Sha, N., Tadesse, M., Yan, K., Pusztai, L. (2010). Gene selection for the identification of biomarkers in high-throughput data. In Bayesian Modeling in Bioinformatics, D. K. Dey, S. Ghosh and B. Mallick (Eds). Chapman and Hall/CRC, 233-254.
4. Kwon, D., Tadesse, M., Sha, N., Pfeiffer, R. and Vannucci, M. (2007). Identifying biomarkers
from mass spectrometry data with ordinal outcomes. Cancer Informatics, 3, 19-28.
5. Sha, N., Tadesse, M. and Vannucci, M. (2006). Bayesian variable selection for the analysis of
microarray data with censored outcomes. Bioinformatics, 22(18), 2262-2268.
6. Tadesse, M., Sha, N., Kim, S. and Vannucci, M. (2006). Identification of biomarkers in classification and clustering of high-throughput data. In Bayesian Inference for Gene Expression and Proteomics, K. A. Do, P. Mueller and M. Vannucci (Eds). Cambridge University Press, 97-115.
7. Moschopoulos, P. and Sha, N. (2005). Bayesian inference of scale parameters in exponential family using conditionally specified priors. Communications in Statistics: Theory and Methods, 34(2): 303-318.
8. Tadesse, M., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of American Statistical Association, 100, 602—617.
9. Vannucci, M., Sha, N. and Brown, P. J. (2005). NIR and mass spectra classification: Bayesian methods for wavelet-based feature selection. Chemometrics and Intelligent Laboratory Systems, 77(1-2), 139-148.
10. Liang, H. and Sha, N. (2004). Modeling antitumor activity by using a nonlinear mixed-effects model. Mathematical Biosciences, 189: 61-73.
11. 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.
12. Lee, K.E., Sha, N., Dougherty, E., Vannucci, M. and Mallick, B.K. (2003). Gene selection: A
Bayesian variable selection approach. Bioinformatics, 19: 90-97.
13. Sha, N., Vannucci, M., Brown, P. J., Trower, M. K., Amphlett, G. and Falciani, F. (2003). Gene
selection in arthritis classification with large-scale microarray expression profiles. Comparative
and Functional Genomics, 4: 171-181.