Ying Hung , Associate Professor
Department of Statistics
email: yhung stat.rutgers.edu
Office: Room 507, Hill Center
L. Huwang and Ying Hung (2007). Effect of Measurement Error on Monitoring Multivariate Process Variability, Statistica Sinica, 17, 749-760.
V. Roshan Joseph and Ying Hung (2008). Orthogonal-Maximin Latin Hypercube Designs, Statistica Sinica, 18, 171-186. C++ code , Read Me
V. Roshan Joseph, Ying Hung, and A. Sudjianto (2008). Blind Kriging: A New Method for Developing Metamodels, ASME (American Society of Mechanical Engineers) Journal of Mechanical Design, 130, 031102-1-8. , R code , Data set
Ying Hung, V. Zarnitsyna, Y. Zhang, C. Zhu, and C. F. Jeff Wu (2008). Binary Time Series Modeling with Application to Adhesion Frequency Experiments, Journal of the American Statistical Association, 103,1248-1259.
W. Tan, I. C. Ume, Ying Hung, and C. F. J. Wu (2008). Effects of Warpage on Fatigue Reliability of Solder Bumps: Experimental and Analytical Studies Proceedings of the 58th Electronic Components and Technology Conference,131-138.
Ying Hung, V. Roshan Joseph, and S. N. Melkote (2009). Design and Analysis of Computer Experiments with Branching and Nested Factors, Technometrics, 51, 354-365.
W. Tan, I. Charles Ume, Ying Hung, and C. F. Jeff Wu (2010). Effects of Warpage on Fatigue Reliability of Solder Bumps: Experimental and Analytical Studies, IEEE Transactions on Advanced Packaging, 33, 314-322.
Ying Hung, Y. Amemiya, and C. F. Jeff Wu (2010). Probability-Based Latin Hypercube Designs, Biometrika, 97, 961-968.
Ying Hung (2011). Penalized Blind Kriging in Computer Experiments, Statistica Sinica, 21, 1171-1190.
Ying Hung (2011). Adaptive Probability-based Latin Hypercube Designs, Journal of the American Statistical Association, 106, 213-219.
Ying Hung (2012). Order Selection in Nonlinear Time Series Models with Application to Cell Adhesion Experiments, Annals of Applied Statistics, 6,1256-1279.
R.-B. Chen, D.-N. Hsieh, Ying Hung, and W. Wang (2013). Optimizing Latin Hypercube Designs by Particle Swarm, Statistics and Computing, 23,663-676.
Ying Hung (2012). Optimal Experiment Design: Latin Hypercube, Encyclopedia of Systems Biology, Springer.
K Wang, C. Zhang, J. Su, B. Wang, and Ying Hung (2013). Optimization of Composite Manufacturing Processes with Computer Experiments and Kriging Methods, International Journal of Computer Integrated Manufacturing, 26, 216-226.
L. Ju, Y. Wang, Ying Hung, C. F. J. Wu, and C. Zhu (2013). An HMM-Based Algorithm for Evaluating Rates of Receptor-Ligand Binding Kinetics from Thermal Fluctuation Data, Bioinformatics, 29,2511-2518.
Ying Hung and V.R. Joseph (2014). Discussion of “Three-Phase Optimal Design of Sensitivity Experiments", Journal of Statistical Planning and Inferences, 149,16-19.
Ying Hung (2014). Sequential Probability-based Latin Hypercube Designs without Replacement, Statistica Sinica, 24,985-1000.
Ying Hung, Y. Wang, V. Zarnitsyna, C. Zhu, and C. F. J. Wu (2013). Hidden Markov Models with Applications in Cell Adhesion Experiments, Journal of the American Statistical Association, 108, 1469-1479.
R.-B. Chen, Y.-W. Hsu, Ying Hung, and W. Wang (2014). Central Composite Discrepancy-Based Uniform Designs for Irregular Experimental Regions, Computational Statistics and Data Analysis, 72, 282-297.
Ying Hung, V. Roshan Joseph, and S. N. Melkote (2015). Analysis of Computer Experiments with Functional Response, Technometrics, 57,33-54.
X. Deng, Ying Hung, and C.D. Lin (2015). Design for Computer Experiments with Qualitative and Quantitative Factors, Statistica Sinica, 25, 1567-1581.
T. Park, B. Yum, Ying Hung, Y.-S. Jeong, and M. K. Jeong (2016). Robust Kriging Models in Computer Experiments, Journal of Operational Research Society, 67, 644-653.
Y. Zhao, Y. Amemiya, and Ying Hung (2017+). Efficient Gaussian Process Modeling using Experimental Design-based Subagging, Statistica Sinica, to appear.
C.-C. Lin and Ying Hung (2018). A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos, Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
R.-B. Chen, C.-H. Li, Ying Hung, W. Wang (2018). Optimal Non-collapsing Space-filling Designs for Irregular Experimental Regions, the Journal of Computational and Graphical Statistics, to appear.
C.-L. Sung, Ying Hung, W. Rittase, C. Zhu, and C. F. J. Wu (2018). A Generalized Gaussian Process Model for Computer Experiments with Binary Time Series, under review by Journal of American Statistical Association.
C.-L. Sung, Ying Hung, W. Rittase, C. Zhu, and C. F. J. Wu (2018). Calibration for Computer Experiments with Binary Responses, under review by Journal of American Statistical Association.
C. Li, Ying Hung, and M. Xie (2018). A Sequential Split-Conquer-Combine Approach for Gaussian Process Modeling in Computer Experiments, submitted.