Outstanding Statistical Application Award

 

Irina Degtiar

Mathematica

Tim Layton

Harvard University

Jacob Wallace

Yale University

Sherri Rose

Stanford University

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 $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 no more than two years prior to the nomination deadline of the award. All nominated work must have been subject to external peer review and accepted for publication by the nomination deadline.

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

2023: Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, Noel Cressie, for their paper, “WOMBAT v1.0: A Fully Bayesian Global Flux-Inversion Framework” Geoscientific Model Development”.
2022: Xu Shi, Xiaoou Li, Tianxi Cai, for their paper, “Spherical Regression Under Mismatch Corruption with Application to Automated Knowledge Translation”
2021: Qian Guan, Brian J. Reich, Eric B. Laber, Dipankar Bandyopadhyay, for their paper “Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals”
2020: Youjin Lee, Katharine Laughon Grantz, Mei-Cheng Wang, Rajeshwari Sundaram, for their paper, “Joint Modeling of Competing Risks Data and Current Status Data: An Application to Spontaneous Labour Study”.
2020: Yiming Hu, Brian W. Kunkle, Mo Li, B. Shubhabrata Mukherjee, Qiongshi Lu, Pradeep Natarajan, Haoyi Weng, Adam Naj, Jiawei Wang, Amanda Kuzma, Seyedeh M. Zekavat, Yi Zhao, Zhaolong Yu, Paul K. Crane, Boyang Li, Alzheimer's Disease Genetics Consortium, Jianlei Gu, Hui Lu, Sydney Muchnik, Hongyu Zhao, Yu Shi, for their paper, “A Statistical Framework for Cross-Tissue Transcriptome-Wide Association Analysis”.
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.