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Author [ Title(Asc)] Type Year
V
Zhu, M. & Fan, G., 2011. Variable selection by ensembles for the Cox model. Journal of Statistical Computation and Simulation, 81, pp.1983–1992. Available at: http://doi.org/10.1080/00949655.2010.511622.
U
Zhu, M. et al., 2014. Using machine learning to plan rehabilitation for home care clients: Beyond "black-box" predictions. In Machine Learning in Healthcare Informatics. Springer, pp. 181–207. Available at: http://doi.org/10.1007/978-3-642-40017-9_9.
Zhu, M. et al., 2007. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Medical Informatics and Decision Making, 7, p.41. Available at: http://doi.org/10.1186/1472-6947-7-41.
Zhu, M., 2015. Use of majority votes in statistical learning. Wiley Interdisciplinary Reviews: Computational Statistics, 7, pp.357–371. Available at: http://doi.org/10.1002/wics.1362.
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Hoshino, R., Oldford, R.W. & Zhu, M., 2010. Two-stage approach for unbalanced classification with time-varying decision boundary: Application to marine container inspection. In Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics. pp. 1:1–1:5. Available at: http://doi.org/10.1145/1938606.1938607.
Parissenti, A.M. et al., 2015. Tumor RNA disruption predicts survival benefit from breast cancer chemotherapy. Breast Cancer Research and Treatment, 153, pp.135–144. Available at: http://doi.org/10.1007/s10549-015-3498-9.
Yuan, Y., Su, W. & Zhu, M., 2015. Threshold-free measures for assessing the performance of medical screening tests. Frontiers in Public Health, 3, p.57. Available at: http://doi.org/10.3389/fpubh.2015.00057.
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Xin, L. & Zhu, M., 2012. Stochastic stepwise ensembles for variable selection. Journal of Computational and Graphical Statistics, 21, pp.275–294. Available at: http://doi.org/10.1080/10618600.2012.679223.
Soltan-Ghoraie, L., Burkowski, F. & Zhu, M., 2015. Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins. Proteins: Structure, Function, and Bioinformatics, 83, pp.497–516. Available at: http://doi.org/10.1002/prot.24752.
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Soltan-Ghoraie, L. et al., 2014. Residue-specific side-chain polymorphisms via particle belief propagation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11, pp.33–41. Available at: http://doi.org/10.1109/TCBB.2013.130.
Su, W., Yuan, Y. & Zhu, M., 2015. A relationship between the average precision and the area under the ROC curve. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval. pp. 349–352. Available at: http://doi.org/10.1145/2808194.2809481.
Hofert, M., Prasad, A. & Zhu, M., Accepted. RafterNet: Probabilistic predictions in multi-response regression. The American Statistician. Available at: https://doi.org/10.1080/00031305.2022.2141857.
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Hofert, M., Prasad, A. & Zhu, M., 2021. Quasi-random sampling for multivariate distributions via generative neural networks. Journal of Computational and Graphical Statistics, 30, pp.647–670. Available at: http://doi.org/10.1080/10618600.2020.1868302.
Wu, Y., Qin, Y. & Zhu, M., 2019. Quadratic discriminant analysis for high-dimensional data. Statistica Sinica, 29, pp.939–960. Available at: http://doi.org/10.5705/ss.202016.0034.
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Su, W., Chipman, H.A. & Zhu, M., 2011. Pseudo-likelihood inference underestimates model uncertainty: Evidence from Bayesian nearest neighbours. Journal of the Iranian Statistical Society, 10, pp.167–180. Available at: http://jirss.irstat.ir/article-1-162-en.html.
Zhang, C., Wu, Y. & Zhu, M., 2019. Pruning variable selection ensembles. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12, pp.168–184. Available at: http://doi.org/10.1002/sam.11410.

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