Stats & Data Science Views has published a number of Lay Abstracts for articles recently published in Canadian Journal of Statistics. The aim is to highlight the latest research to a broader audience in an accessible format. Read this collection of abstracts published between February 2021 and August 2021.

• Maiti, T., Safikhani, A. and Zhong, P.‐S. (2021), On uncertainty estimation in functional linear mixed models. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11585
• Yuan, M., Li, P. and Wu, C. (2021), Semiparametric inference of the Youden index and the optimal cut‐off point under density ratio models. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11600
• Dai, N., Kang, H., Jones, G.L., Fiecas, M.B. and (2021), A Bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer’s disease. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11588
• Mohammed, S. and Dey, D.K. (2021), Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11592
• Wang, Z., Sair, H.I., Crainiceanu, C., Lindquist, M., Landman, B.A., Resnick, S., Vogelstein, J.T. and Caffo, B. (2021), On statistical tests of functional connectome fingerprinting. Lay abstract Can J Statistics. https://doi.org/10.1002/cjs.11591
• Hart, B., Malone, S. and Fiecas, M. (2021), A grouped beta process model for multivariate resting‐state EEG microstate analysis on twins. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11589
• Fan, Y., Liu, Y. and Zhu, L. (2021), Optimal subsampling for linear quantile regression models. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11590
• Diao, G. and Qin, J. (2021), New semiparametric regression method with applications in selection-biased sampling and missing data problems. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11615
• Hu, J., Chen, Y., Zhang, W. and Guo, X. (2021), Penalized high-dimensional M-quantile regression: From L¹ to L^p optimization. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11597
• Qian, F., Zhang, W. and Chen, Y. (2021), Adaptive banding covariance estimation for high-dimensional multivariate longitudinal data. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11598
• Yu, J. and Lai, D. (2021), Interim analysis of sequential estimation-adjusted urn models with sample size re-estimation. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11609
• Brunel, E. and Comte, F. (2021), Hazard regression with noncompactly supported bases. Can J Statistics. Lay abstract https://doi.org/10.1002/cjs.11619