Sudipto Banerjee, Professor and Chair of the Department of Biostatistics, and his coauthors are recipients of the American Statistical Association's Outstanding Statistical Application Award for 2017...

Sudipto Banerjee, Professor and Chair of the Department of Biostatistics, and his coauthors are recipients of the American Statistical Association's Outstanding Statistical Application Award for 2017 for their paper, Non-separable Dynamic Nearest-Neighbor Gaussian Process Models for Spatio-Temporal Data With an Application to Particulate Matter Analysis1. This award, established in 1986, is to recognize the authors of papers that demonstrate an outstanding application of statistics in any substantive field. Award recipients will be presented with an engraved award and $1,000 that is divided evenly among the winners during the 2017 JSM in Baltimore, Maryland. The award is bestowed upon a distinguished individual or individuals based on the following criteria: (a) the impact of the statistical application in addressing a significant problem in a substantive field, and (b) the ingenuity and or novelty of the statistical treatment of the problem.

1Abhirup Datta, Sudipto Banerjee, Andrew O. Finley, Nicholas A. S. Hamm, and Martijn Schaap (2016). Non-separable Dynamic Nearest-Neighbor Gaussian Process Models for Spatio-Temporal DataWith an Application to Particulate Matter Analysis. Annals of Applied Statistics, 10: 1286-1316.