References
- C. Ju, A. Bibaut, and M. van der Laan, The relative performance of ensemble methods with deep convolutional neural networks for image classification. J. Appl. Stat. 45 (2018), pp. 2800–2818. doi: 10.1080/02664763.2018.1441383
- L.C. Matioli, S.R. Santos, M. Kleina, and E.A. Leite, A new algorithm for clustering based on kernel density estimation. J. Appl. Stat. 45 (2018), pp. 347–366. doi: 10.1080/02664763.2016.1277191
- D. McNeish, Missing data methods for arbitrary missingness with small samples. J. Appl. Stat. 44 (2017), pp. 24–39. doi: 10.1080/02664763.2016.1158246
- G. Onicescu, A.B. Lawson, J. Zhang, M. Gebregziabher, K. Wallace, and J.M. Eberth, Spatially explicit survival modeling for small area cancer data. J. Appl. Stat. 45 (2018), pp. 568–585. doi: 10.1080/02664763.2017.1288200
- C. Peng, M. Xu, S. Xu, and T. Hu, Modeling and predicting extreme cyber attack rates via marked point processes. J. Appl. Stat. 44 (2017), pp. 2534–2563. doi: 10.1080/02664763.2016.1257590
- M. Stehlík, L.M. Grilo, and P.K. Jordanova, Editorial to special issue V WCDANM 2018. J. Appl. Stat. 47 (2020), pp. 2289–2298. doi: 10.1080/02664763.2020.1818489
- C. Stewart, An approach to measure distance between compositional diet estimates containing essential zeros. J. Appl. Stat. 44 (2017), pp. 1137–1152. doi: 10.1080/02664763.2016.1193846