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Journal of Applied Statistics Best Paper Prize

Journal of Applied Statistics

About the Prize

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.

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.

Visit Journal Articles

Year Author(s) Article Volume Issue
2019 - Winner Cheng Ju et al. Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods 46 12
2019 - Winner J. Pecanka, A. W. van der Vaart & M. A. Jonker Modeling association between multivariate correlated outcomes and high-dimensional sparse covariates: the adaptive SVS method 46 5
2019 - Highly Commended Marcelo Bourguignon, Josemar Rodrigues & Manoel Santos-Neto Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion 46 1
2019 - Highly Commended Antonio Punzo A new look at the inverse Gaussian distribution with applications to insurance and economic data 46 7
2018 - Winner Georgiana Onicescu et al. Spatially explicit survival modeling for small area cancer data 45 3
2018 - Highly Commended Cheng Ju, Aurélien Bibaut, Mark van der Laan The relative performance of ensemble methods with deep convolutional neural networks for image classification 45 15
2018 - Highly Commended Matioli, L. C.; Santos, S. R.; Kleina, M.; Leite, E. A. A new algorithm for clustering based on kernel density estimation 45 2
2017-Highly Commended Chen Peng, Maochao Xu, Shouhuai Xu & Taizhong Hu Modeling and predicting extreme cyber attack rates via marked point processes 44 14
2017-Highly Commended Daniel McNeish Missing data methods for arbitrary missingness with small samples 44 1

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