I am interested in a wide variety of disciplines within Statistics. These include, but are not limited to, maximum likelihood estimation, exponential family theory, generalized linear models, envelope methodology, conformal prediction, causal inference, bootstrap techniques, and multivariate statistics. I am generally interested in the tradeoffs between robustness and efficiency in estimation. My CV is attached here.
My research mission is to improve statistical methodologies that are applicable to real-world problems. My focus is placed on both the theoretical and computational aspects of this methodology. To better understand relevant real-world problems, I work closely with scientists and researchers across a variety of disciplines. Writing technical research papers, or an accompanying technical report, that can be understood by the intended scientific audience is central to my research mission. Current methodological work of mine has applications in evolutionary biology, engineering, epidemiology, chemometrics, and several domains of physics.
Daniel J. Eck, Charles J. Geyer, and R. Dennis Cook (2019). Combining Envelope Methodology and Aster Models for Variance Reduction in Life History Analyses. Minor revision requested at Journal of Statistical Planning and Inference.
Daniel J. Eck, R. Dennis Cook, Christopher Nachtsheim, and Thomas A. Albrecht (2019). Multivariate Design of Experiments for Engineering Dimensional Analysis. In press at Technometrics.
Daniel J. Eck (2018). Bootstrapping for multivariate linear regression models. Statistics and Probability Letters, 134, pgs 141-149.
Rickard J. Kohler, Susan A. Arnold, Daniel J. Eck, Chris Thomson, Matthew A. Hunt, and G. Elizabeth Pluhar (2018). Incidence of and risk factors for major complications or death in dogs undergoing cytoreductive surgery for treatment of suspected primary intracranial masses. Journal of the American Veterinary Medical Association, 253, 12, pgs 1594-1603.
Daniel J. Eck and R. Dennis Cook (2017). Weighted envelope estimation to handle variability in model selection. Biometrika, 104, 3, pgs 743-749. Software that implements the methods in this paper was created by Minji Lee and Zhihua Su, see: Renvlp.
Daniel J. Eck and Ian W. McKeague (2016). Central Limit Theorems under additive deformations. Statistics and Probability Letters, 118, pgs 156-162.
Daniel J. Eck, Ruth G. Shaw, Charles J. Geyer, and Kingsolver, Joel G. (2015). An Integrated Analysis of Phenotypic Selection on Insect Body Size and Development Time. Evolution, 69, pgs 2525-2532.
Daniel J. Eck and Charles J. Geyer (2019+). Computationally efficient likelihood inference in exponential families when the maximum likelihood estimator does not exist and Supplementary Materials.
Daniel J. Eck, Olga Morozova, and Forrest W. Crawford (2019+). Randomization for the direct effect of an infectious disease intervention in a clustered study population.
Si Cheng, Daniel J. Eck, and Forrest W. Crawford (2019+). Estimating the size of a hidden finite set: large-sample behavior of estimators
Daniel J. Eck and Forrest W. Crawford (2019+). Conformal prediction for exponential families and generalized linear models.
Daniel J. Eck (2019). Technical report for “Conformal prediction for exponential families and generalized linear models”.
Daniel J. Eck, Charles J. Geyer, and R. Dennis Cook (2018). Web-based Supplementary Materials for “Combining Envelope Methodology and Aster Models for Variance Reduction in Life History Analyses”.
Daniel J. Eck, Ruth G. Shaw, Charles J. Geyer, and Joel G. Kingsolver (2015). Supporting Data Analysis for “An Integrated Analysis of Phenotypic Selection on Insect Body Size and Development Time”.
Morozova, O., Daniel J. Eck, and Forrest W. Crawford (2019+). Regression and stratification for contagious outcomes.
Daniel J. Eck (2019+). Model-free weighted envelope estimation.
First of all, I have found all of the projects that I have worked on to be fun projects. That being said, I have always had a strong interest in baseball. I enjoy using statistics to improve upon our understanding of baseball and baseball’s greatest players.
Here is a paper that shows that old time baseball players are overrated compared to more modern day players:
Daniel J. Eck (2019). Challenging nostalgia and performance metrics in baseball. To appear at Chance (let me know if you were not mentioned in the acknowledgements).
I am working on a new direct estimator of wins above replacement (WAR) using techniques from causal inference. Let me know if you are interested in working with me on this project.