Stats & Data Science Views has published a number of Layman’s 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 in December 2020.
- Liu, Y., Xu, J. and Li, G. (2021), Sure joint feature screening in nonparametric transformation model for right censored data. Can J Statistics. https://doi.org/10.1002/cjs.11575 Layman’s Abstract
- Li, Y. and Deng, X. (2020), An efficient algorithm for Elastic I‐optimal design of generalized linear models. Can J Statistics. https://doi.org/10.1002/cjs.11571 Layman’s Abstract
- Betsch, S., Ebner, B. and Klar, B. (2020), Minimum Lq‐distance estimators for non‐normalized parametric models. Can J Statistics. https://doi.org/10.1002/cjs.11574 Layman’s Abstract
- Salamanca, J.J. (2020), Sets that maximize probability and a related variational problem. Can J Statistics. https://doi.org/10.1002/cjs.11578 Layman’s Abstract
- Yuan, A., Piao, J., Ning, J. and Qin, J. (2020), Semiparametric isotonic regression modelling and estimation for group testing data. Can J Statistics. https://doi.org/10.1002/cjs.11581 Layman’s Abstract
- Casleton, E.M., Nordman, D.J. and Kaiser, M.S. (2020), Local structure graph models with higher‐order dependence. Can J Statistics. https://doi.org/10.1002/cjs.11573 Layman’s Abstract