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Author [ Title(Asc)] Type Year
E
Murdoch, W.J. & Zhu, M., 2016. Expanded alternating optimization for matrix factorization and penalized regression. In Proceedings of the 22nd International Conference on Computational Statistics. pp. 217–229. Available at: http://www.compstat2016.org/docs/COMPSTAT2016_proceedings.pdf?20160807205859.
D
Hastie, T.J. & Zhu, M., 2001. Discussion of "Dimension reduction and visualization in discriminant analysis" by Cook and Yin. Australian and New Zealand Journal of Statistics, 43, pp.179–185.
Zhu, M., 2006. Discriminant analysis with common principal components. Biometrika, 93, pp.1018–1024. Available at: http://doi.org/10.1093/biomet/93.4.1018.
Fan, G. & Zhu, M., 2011. Detection of rare items with TARGET. Statistics and Its Interface, 4, pp.11–17. Available at: http://doi.org/10.4310/SII.2011.v4.n1.a2.
Hofert, M., Prasad, A. & Zhu, M., Accepted. Dependence model assessment and selection with DecoupleNets. Journal of Computational and Graphical Statistics. Available at: https://doi.org/10.1080/10618600.2022.2157835.
C
Zhu, M. & Lu, A.Y., 2004. The counter-intuitive non-informative prior for the Bernoulli family. Journal of Statistics Education, 12, p.2. Available at: http://doi.org/10.1080/10691898.2004.11910734.
Xin, L., Zhu, M. & Chipman, H.A., 2017. A continuous-time stochastic block model for basketball networks. Annals of Applied Statistics, 11, pp.553–597. Available at: http://doi.org/10.1214/16-AOAS993.
Nguyen, J. & Zhu, M., 2013. Content-boosted matrix factorization techniques for recommender systems. Statistical Analysis and Data Mining: The ASA Data Science Journal, 6, pp.286–301. Available at: http://doi.org/10.1002/sam.11184.
Forbes, P. & Zhu, M., 2011. Content-boosted matrix factorization for recommender systems: Experiments with recipe recommendation. In Proceedings of the 5th ACM Conference on Recommender Systems. pp. 261–264. Available at: http://doi.org/10.1145/2043932.2043979.
Zhu, M., Hastie, T.J. & Walther, G., 2005. Constrained ordination analysis with flexible response functions. Ecological Modelling, 187, pp.524–536. Available at: http://doi.org/10.1016/j.ecolmodel.2005.01.049.
Young, S.S., Yuan, F. & Zhu, M., 2012. Chemical descriptors are more important than learning algorithms for modelling. Molecular Informatics, 31, pp.707–710. Available at: http://doi.org/10.1002/minf.201200031.
A
Zhu, M. & Ghodsi, A., 2006. Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis, 51, pp.918–930. Available at: http://doi.org/10.1016/j.csda.2005.09.010.
Hofert, M., Prasad, A. & Zhu, M., 2022. Applications of multivariate quasi-random sampling with neural networks. In Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2020. Springer, pp. 273–289. Available at: https://doi.org/10.1007/978-3-030-98319-2_14.

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