洪 弘 / Hung Hung
Institute of Health Data Analytics and Statistics
National Taiwan University
Email: hhung@ntu.edu.tw
RESEARCH INTERESTS
Dimension Reduction Analysis
Robust Statistical Methods
EDUCATIONAL BACKGROUND
PhD in Mathematics, National Taiwan University (2009)
MS in Industrial Engineering, National Taiwan University (2004)
BS in Business Administration, National Taiwan University (2002)
PROFESSIONAL EXPERIENCE
Professor, Institute of Health Data Analytics and Statistics, National Taiwan University. (2022/8 - present)
Professor, Institute of Epidemiology and Preventive Medicine, National Taiwan University. (2019/8 - 2022/7)
Associate Professor, Institute of Epidemiology and Preventive Medicine, National Taiwan University. (2014/8 - 2019/7)
Assistant Professor, Institute of Epidemiology and Preventive Medicine, National Taiwan University. (2010/2 - 2014/7)
ACADEMIC PUBLICATIONS
Hung, H. and Huang, S. Y. (2023). On the efficiency-loss free ordering-robustness of product-PCA. Submitted. Arxiv.
Hung, H., Huang, S. Y., and Eguchi, S. (2023). Robust self-tuning semiparametric PCA for contaminated elliptical distribution. IEEE Transactions on Signal Processing, 70, 5885-5897. code
Hung, H., Huang, S. Y., and Ing, C. K. (2022). A generalized information criterion for high-dimensional PCA rank selection. Statistical Papers, 63, 1295–1321. code
Huang, M. Y. and Hung, H. (2022). A Review on sliced inverse regression, sufficient dimension reduction, and applications. Statistica Sinica, 32, 2297-2314.
Li, C. J., Huang, P. H., Ma, Y. T., Hung, H., and Huang, S. Y. (2022). Robust aggregation for federated learning by minimum gamma-divergence estimation. Entropy, 24(5), 686.
Hung, H. (2019). A robust removing unwanted variation-testing procedure via gamma-divergence. Biometrics 75, 650-662. code
Hung, H. and Huang, S. Y. (2019). Sufficient dimension reduction via random-partitions for the large-p-small-n problem. Biometrics 75, 245-255. code
Hung, H. and Jou, Z. Y. (2019). A low-rank based estimation-testing procedure for matrix-covariate regression. Statistica Sinica 29, 1025-1046.
Hung, H., Jou, Z. Y., and Huang, S. Y. (2018). Robust mislabel logistic regression without modeling mislabel probabilities. Biometrics 74, 145-154. code
Hung, H. and Lu, H. H. (2017). A review on the generalization of sufficient dimension reduction methods with the additional information. WIREs Computational Statistics, e1401. (doi: 10.1002/wics.1401)
Hung, H., Liu, C. Y., and Lu, H. S. (2016). Sufficient dimension reduction with additional information. Biostatistics 17, 405-421.
Hung, H., Lin, Y. T., Wang, C. C., Chen, P., Huang, S. Y., and Tzeng, J. Y. (2016). Detection of gene-gene interactions using multistage sparse and low-rank regression. Biometrics 72, 85-94.
Chen, T. L., Hsieh, D. N., Hung, H., Tu, I. P., Wu, P. S., Wu, Y. M., Chang, W. H., and Huang, S. Y. (2014). gamma-SUP: a clustering algorithm for cryo-electron microscopy images of asymmetric particles. The Annals of Applied Statistics 8, 259-285. code
Hung, H. and Wang, C. C. (2013). Matrix variate logistic regression model with application to EEG data. Biostatistics 14, 189-202. code
Hung, H. and Wang, C. C. (2013). Rejoinder to doi:10.1093/biostatistics/kxs039. Biostatistics 14, 406-407.
Chen, P., Hung, H., Komori, O., Huang, S. Y., and Eguchi, S. (2013). Robust independent component analysis via minimum gamma-divergence estimation. IEEE Journal of Selected Topics in Signal Processing 7, 614-624.
Liu, J. R., Kuo, P. H., Hung, H. (2013). A robust re-rank approach for feature selection and its application to pooling-based GWA study. Computational and Mathematical Methods in Medicine, Article ID: 860673.
Hung, H. (2012). A two-stage dimension reduction method for transformed responses and its applications. Biometrika 99, 865-877.
Hung, H., Wu, P. S., Tu, I. P., and Huang, S. Y. (2012). On multilinear principal component analysis of order-two tensors. Biometrika 99, 569-583. code
Hung, H. and Chen, A. (2012). Test of covariance changes without a large sample and its application to fault detection and classification. Journal of Process Control 22, 1113-1121.
Hung, H. and Chiang, C. T. (2011). Nonparametric methodology for the time-dependent partial area under the ROC curve. Journal of Statistical Planning and Inference 141, 3829-3838.
Hung, H. and Huang, S. Y. (2010). Discussion of "Envelope models for parsimonious and efficient multivariate linear regression''. Statistica Sinica 20, 981-987.
Hung, H. and Chiang, C. T. (2010). Optimal composite markers for time-dependent receiver operating characteristic curves with censored survival data. Scandinavian Journal of Statistics 37, 664-679.
Hung, H. and Chiang, C. T. (2010). Estimation methods for time-dependent AUC models with survival data. Canadian Journal of Statistics 38, 8-26.
Chiang, C. T. and Hung, H. (2010). Nonparametric estimation for time-dependent AUC, Journal of Statistical Planning and Inference 140, 1162-1174.
Chiang, C. T., Wang, S. H., and Hung, H. (2009). Random weighting and edgeworth expansion for the nonparametric time-dependent AUC estimator. Statistica Sinica 19, 969-979.