Outstanding Statistical Application Award

About the Award

The Outstanding Statistical Application Award was established in 1986 to recognize the authors of papers that demonstrate an outstanding application of statistics in any substantive field. Award recipients are presented with an engraved award and $1,000 which may be divided evenly among the recipients or, the award committee may recognize an additional $1,000 winner each year if they wish. The award is presented annually if, in the opinion of the awards committee, an eligible and worthy work is nominated.

Selection Criteria

The Outstanding Statistical Application Award is bestowed upon a distinguished individual or individuals based on the following criteria:

• The impact of the statistical application in addressing a significant problem in a substantive field
• The ingenuity and or novelty of the statistical treatment of the problem

Eligible work includes papers, monographs, reports, and other substantive evidence appearing within two years of the presentation of the award. All nominated work must have been subject to external peer review and, preferably, formal refereeing.

Award Recipient Responsibilities

The award recipient(s) are responsible for providing a current photograph and general information, along with permission to reprint the work in ASA promotional materials.

Nominations

Nominations are due by March 1 and require the following:

• Nominating letter—not to exceed two pages—describing the paper's significance, particularly its impact on the substantive field
• Copy of the nominated work
• Candidate’s headshot

Questions

Please contact the committee chair.

Recent Award Recipients

2019: Liangyuan Hu, Joseph Hogan, Ann Mwangi, Abraham Siika, for their novel development and applications of statistical approaches to assess the causal effects of treatment initiation time on patient survival using electronic health records (EHR). The authors developed a structural causal hazard model for drawing causal inferences about the optimal timing of treatment initiation on a continuous scale using observational EHR data. Their work leads to impactful outcomes in medical practice. Their paper, “Modeling the Causal Effect of Treatment Initiation Time on Survival: Application to HIV/TB Co-Infection,” was published in Biometrics in 2018.
2018: Peijie Hou, Joshua M. Tebbs, Christopher R. Bilder, and Christopher S. McMahan for their novel development of a statistical framework that quantifies the operating characteristics of hierarchical group testing for multiple rare diseases. The authors cleverly reformulate the pool decoding process as a time‐inhomogeneous, finite‐state Markov chain and provide analytical solutions of prediction accuracy and the expected number of tests. Their methodology is having tremendous impact on public health screening, and their paper, “Hierarchical Group Testing for Multiple Infections,” was published in Biometrics in 2017.
2017: Abhirup Datta, Sudipto Banerjee, Andrew O. Finley, Nicholas A. S. Hamm, and Martijn Schaap for “Non-Separable Dynamic Nearest-Neighbor Gaussian Process Models for Spatio-Temporal Data with an Application to Particulate Matter Analysis,” published in the Annals of Applied Statistics in 2016.
2016: Edoardo M. Airoldi and Johnathan M. Bischof for “A Regularization Scheme on Word Occurrence Rates That Improves Estimation and Interpretation of Topical Content,” published in the Journal of the American Statistical Association in 2015.
2015: Anne R. Cappola, Leslie J. Crofford, Wensheng Guo, and Ziyue Liu for “Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models,”, published in the Journal of the American Statistical Association in 2014.
2014: Christopher R. Bilder, Christopher S. McMahan, and Joshua M. Tebbs for “Two-Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility Prevention Project,” published in Biometrics in 2013.
2013: Robert E. Kass, Ryan C. Kelly, and Wei-Liem Loh for “Assessment of Synchrony in Multiple Neural Spike Trains Using Loglinear Point Process Models,” published in The Annals of Applied Statistics in 2011.
2012: Chae Young Lim and Sarat C. Dass for “Assessing Fingerprint Individuality Using EPIC: A Case Study in the Analysis of Spatially Dependent Marked Processes,” published in Technometrics in 2011.
2011: Adrian E. Raftery, Miroslav Kárný, and Pavel Ettler