Export 1668 results:
2023
Zhang, Y., Khalilitousi, M.(sam) & Park, Y.P., 2023. Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling. Cell Genomics, 3, p.100388.
2022
Isberg, S. & Welch, W.J., 2022. Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code. Journal of Computational and Graphical Statistics, pp.1-12. Available at: https://doi.org/10.1080/10618600.2022.2039160.
Dean, C.B. et al., 2022. Canadian contributions to environmetrics. Canadian Journal of Statistics, n/a. Available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.11743.
He, M. & Chen, J., 2022. Consistency of the MLE under two-parameter mixture models with a structural scale parameter. Advances in Data Analysis and Classification, 16, pp.125-154. Available at: http://doi.org/10.1007/s11634-021-00472-5.
Zhang, A.Gong & Chen, Jand, 2022. Density ratio model with data-adaptive basis function. Journal of Multivariate Analysis, 191.
Zhang, Q. & Chen, J., 2022. Distributed learning of finite Gaussian mixtures. Journal of Machine Learning Research, 23, pp.1-40. Available at: \urlhttp://jmlr.org/papers/v23/21-0093.html.
Cramer, E.Y. et al., 2022. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119, p.e2113561119. Available at: https://www.pnas.org/doi/abs/10.1073/pnas.2113561119.
Chen, J. et al., 2022. Permutation tests under a rotating sampling plan with clustered data. Ann. Appl. Statist., 16, pp.936-958.
Chen, J. et al., 2022. Permutation tests under a rotating sampling plan with clustered data. The Annals of Applied Statistics, 16, pp.936–958.
Ding, L., Zentner, G.E. & McDonald, D.J., 2022. Sufficient principal component regression for genomics. Bioinformatics Advances, 2, p.vbac033. Available at: https://doi.org/10.1093/bioadv/vbac033.
2021
Zhao, T. & Bouchard-Côté, A., 2021. Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler. Journal of Machine Learning Research, 22, pp.1–41.
Wang, Y., Le, N.D. & Zidek, J.V., 2021. Approximately Optimal Subset Selection for Statistical Design and Modelling. Journal of Statistical Computation and Simulation, pp.1-13.
Bouchard-Côté, A. et al., 2021. Blang: Probabilitistic Programming for Combinatorial Spaces. Journal of Statistical Software, (Accepted).
McDonald, D.J. et al., 2021. Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?. Proceedings of the National Academy of Sciences, 118, p.e2111453118. Available at: https://doi.org/10.1073/pnas.2111453118.
Park, Y.P. & Kellis, M., 2021. CoCoA-diff: counterfactual inference for single-cell gene expression analysis. Genome Biol., 22, pp.1–23.
Chen, J. et al., 2021. Composite empirical likelihood for multisample clustered data. J Nonparametric Statistics, p.Accepted Apr 2021.
Chen, J. et al., 2021. Composite empirical likelihood for multisample clustered data. Journal of Nonparametric Statistics, 33, pp.60–81.
Pan, S. et al., 2021. Ellipse detection and localization with applications to knots in sawn lumber images. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 16(2), pp.3892-3901.
Zhang, A.Gong & Chen, J., 2021. Empirical likelihood ratio test on quantiles under a density ratio model. Electronic Journal of Statistics, 15(2), pp.6191-6227. Available at: https://doi.org/10.1214/21-EJS1943.
Policastro, R.A. et al., 2021. Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR. NAR Genomics and Bioinformatics, 3, pp.1–10. Available at: https://doi.org/10.1093/nargab/lqab051.

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