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Mary Gregg (Fed)
Mathematical Statistician
Mary Gregg received a M.S. and a Ph.D. in Biostatistics from the University of Louisville, where her dissertation work focused on reweighting methods that correct for informative cluster size and informative intra-cluster group sizes in the analysis of clustered observations. Mary joined NIST in the fall of 2020 as a National Research Council postdoctoral associate, and as a permanent employee in 2022. Her work at NIST includes interdisciplinary research relating to forensics, wireless communication, and statistical machine learning applications.
Awards
- 2024 ITL Outstanding Collaboration, NIST
For initiating and leading a productive collaboration across multiple NIST disciplines to advance the metrology of terrestrial laser scanners. - 2020-2022 Postdoctoral Associateship, National Academy of Science/National Research Council
SELECTED EXTERNAL PUBLICATIONS
Gregg, M, Datta, S., Lorenz, D. (2022). htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size. The R Journal. 14(2), 54-66. DOI:10.32614/RJ-2022-024.
Gregg, M., Datta, S., Lorenz, D. (2020). Variance Estimation in Tests of Clustered Categorical Data with Informative Cluster Size. Statistical Methods in Medical Research. 29(11), 3396-3408. DOI: 10.1177/0962280220928572.
Gregg, M., Datta, S., Lorenz, D. (2018). A Log Rank Test for Clustered Data with Informative Within-Cluster Group Size. Statistics in Medicine. 37(27), 4071-4082. DOI: 10.1002/sim.7899.