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Journal of Applied Statistics Best Paper Prize

Journal of Applied Statistics

About the prize

The 2018 Journal of Applied Statistics Best Paper Prize had been decided.

 

The award goes to Georgiana Onicescu, Andrew B. Lawson, Jiajia Zhang, Mulugeta Gebregziabher, Kristin Wallace & Jan M. Eberth for their paper: ‘Spatially explicit survival modelling for small area cancer data

 

This award was decided by the Editor-in-Chief and this year’s Award Selection Committee which found that this paper ‘advances the state-of-art applied statistics methodology by combining spatial statistics with survival analysis to model survival data that contain geographical information. The approach is interesting and innovative. Publicly available survival data may contain geographical information, and the paper demonstrates how to use geographical information for the novel modeling of survival data.’

Journal of Applied Statistics

Journal of Applied Statistics is a world-leading journal which provides a forum for communication among statisticians and practitioners for judicious application of statistical principles and innovations of statistical methodology motivated by current and important real-world examples across a wide range of disciplines.

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Journal of Applied Statistics Best Paper Prize promotion

The Journal of Applied Statistics Best Paper Prize is awarded annually, as decided by the Editor-in-Chief with the support of the Associate Editors.

The winning article receives a £500 prize, and their paper will be made free to view for the following year. All articles published in the Journal are automatically included for consideration.

Submit your paper here.

YearAuthor(s)ArticleVolumeIssue
2018 - Highly CommendedCheng Ju, Aurélien Bibaut, Mark van der Laan The relative performance of ensemble methods with deep convolutional neural networks for image classification4515
2018 - Highly CommendedMatioli, L. C.; Santos, S. R.; Kleina, M.; Leite, E. A.A new algorithm for clustering A new algorithm for clustering based on kernel density estimation452
2017-Highly CommendedChen Peng, Maochao Xu, Shouhuai Xu & Taizhong HuModeling and predicting extreme cyber attack rates via marked point processes4414
2017-Highly CommendedDaniel McNeishMissing data methods for arbitrary missingness with small samples441

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