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2019
Watson, J., Zidek, J.V. & Shaddick, G., 2019. A General Theory for Preferential Sampling in Environmental Networks. Annals of Applied Statistics, p.Accepted.
Watson, J., V. Zidek, J. & Shaddick, G., 2019. A general theory for preferential sampling in environmental networks. Annals of Applied Statistics, pp.2662-2700.
Dinsdale, D.R. & Salibian-Barrera, M., 2019. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C, 68(1), p.198. Available at: https://dx.doi.org/10.1111/rssc.12286 .
Fu, E. & Heckman, N., 2019. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis, 131. Available at: https://arxiv.org/abs/1712.07265.
Dinsdale, D.R. & Salibian-Barrera, M., 2019. Modelling ocean temperatures from bio-probes under preferential sampling. Annals of Applied Statistics, 13(2), pp.713-745. Available at: https://arxiv.org/abs/1901.02630.
Zhu, G. & Chen, J., 2019. Multi-parameter One-Sided Monitoring Tests. Technometrics, 60. Available at: 10.1093/biomet/asy068.
Kingwell, E. et al., 2019. Multiple sclerosis: Effect of beta-interferon treatment on survival. Brain, 142, pp.1324-1333.
Luo, H. et al., 2019. A new perspective on the benefits of the gene-environment independence in case-control studies. The Canadian Journal of Statistics, 47, pp.473–486.
Krupskii, P. & Joe, H., 2019. Nonparametric estimation of multivariate tail probabilities and tail dependence coefficients. Journal of Multivariate Analysis, 172, pp.147-161.
Chang, B. & Joe, H., 2019. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis, 139, pp.45-63.
Chang, B. & Joe, H., 2019. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis, 139, pp.45–63.
Cohen-Freue, G.V. et al., 2019. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics, 13(4), pp.2065-2090. Available at: http://dx.doi.org/10.1214/19-AOAS1269.
Cornish, R. et al., 2019. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. In International Conference on Machine Learning (ICML). International Conference on Machine Learning (ICML). pp. 1351–1360.
Hadley, D., Joe, H. & Nolde, N., 2019. On the selection of loss severity distributions to model operational risk. Journal of Operational Risk, 14, pp.73-94.
Zhuang, W.W., Hu, B. & Chen, J., 2019. Semiparametric inference for the dominance index under the density ratio model. Biometrika, 106, pp.229–241.
Chen, J. & Liu, Y., 2019. Small area quantile estimation. International Statistical Review, 87, pp.S219–S238.
Chen, Z., Chen, J. & Zhang, Q., 2019. Small area quantile estimation via spline regression and empirical likelihood. Survey Methodology 45-1, 45, pp.81–99.
Joe, H. & Li, H., 2019. Tail densities of skew-elliptical distributions. Journal of Multivariate Analysis, 171, pp.421-435.
Fernandez-Fontelo, A. et al., 2019. Untangling serially dependent underreported count data for gender-based violence. Statistics in Medicine, 38, pp.4404-4422.
Chang, B., Pan, S. & Joe, H., 2019. Vine Copula Structure Learning via Monte Carlo Tree Search. In International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics.
Chang, B., Pan, S. & Joe, H., 2019. Vine copula structure learning via Monte Carlo tree search. In K. Chaudhuri & Sugiyama, M. , eds. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. pp. 353-361.
2018
Campbell, T. & Broderick, T., 2018. Bayesian coreset construction via greedy iterative geodesic ascent. In International Conference on Machine Learning. International Conference on Machine Learning.

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