I am a survey statistician and a data scientist whose scholarly activities include survey methodological developments, integrative research, applied statistical work, and software development. I greatly enjoy collaborative work and have active collaborations with the US Bureau of Labor Statistics and the US Forest Inventory and Analysis Program. During the summer, I usually supervise undergraduate research students on projects related to forestry data science. This work is supported by the US Forest Inventory and Analysis Program.
I spent my most recent sabbatical as a Visiting Research Scientist at the Rocky Mountain Research Station of the USDA Forest Service Forest Inventory and Analysis Program. For my previous sabbatical, I was an ASA/NSF/BLS Research Fellow in Washington, DC.
Scientific Writing
White, G.W., Yamamoto, J.K., Elsyad, D.H., Schmitt, J.H., Korsgaard, N.H., Hu, J.K.,Gaines, G.C.III, Frescino, T.S. and K.S. McConville. (2025) Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data. Canadian Journal of Forest Research. https://doi.org/10.1139/cjfr-2024-0149
McConville, K.S. (2025) Chapter 7: Multivariate Thinking. MAA Notes - Using Data-Centric Methods to Teach Introductory Statistics, Volume 99. https://maa.org/resource/notes-volume-99-using-data-centric-methods-to-teach-introductory-statistics/
Toth, D. and McConville, K. S. (2024) Design consistent random forest models for data collected from a complex sample. Survey Methodology. 50(2), 185-207. http://www.statcan.gc.ca/pub/12-001-x/2024002/article/00015-eng.pdf.
May, P., McConville, K. S., Moisen, G. G., Bruening, J., and R. Dubayah. (2023) A spatially varying model for small area estimates of biomass density across the contiguous United States. Remote Sensing of Environment. 286: 113420. https://doi.org/10.1016/j.rse.2022.113420
Moisen, G.G., Andersen, H.E., Bell, D.M., McConville, K.S., McRoberts, R.E., Patterson, P.L., Westfall, J.A., and B.T. Wilson. Chapter 7: Emerging Alternative Estimators. Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program: 2022. Northern Research Station
Frescino, T. S., McConville, K. S., White, G. W., J. C. Toney and G. G. Moisen. (2022) Small Area Estimates for National Applications: A Database to Dashboard Strategy using FIESTA. Frontiers in Forests and Global Change. https://doi.org/10.3389/ffgc. 2022.779446
Frachtenberg, E. and McConville, K.S. (2022) Metrics and Methods in the Evaluation of Prestige Bias in Peer Review: A Case Study in Computer Systems Conferences. PLOS ONE 17:2, e0264131. https://doi.org/10.1371/journal.pone.0264131
Wojcik, O.C., Olson, S.D., Nguyen, P-H.V., McConville, K.S., Moisen, G.G., and T.S. Frescino. (2022) GREGORY: A Modified Generalized Regression Estimator Approach to Estimating Forest Attributes in the Interior Western US. Frontiers in Forests and Global Change. https://www.frontiersin.org/articles/10.3389/ffgc.2021.763414
White G.W., McConville K.S., Moisen G.G., Frescino T.S. (2021) Hierarchical Bayesian Small Area Estimation Using Weakly Informative Priors in Ecologically Homogeneous Areas of the Interior Western Forests. Frontiers in Forests and Global Change. 4, 178, https://www.frontiersin.org/article/10.3389/ffgc.2021.752911
Moisen, G.G., McConville, K. S., Schroeder, T. A., Healey, S. P., Finco, M. V., and T. S. Frescino. (2020) Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? Forests 11:8, 856, https://doi.org/10.3390/f11080856
Nolan, J., McConville, K. S., Addona, V., Tintle, N., and D. Pearl. (2020) Mentoring Un- dergraduate Research in Statistics: Reaping the Benefits and Overcoming the Barriers. Journal of Statistics and Data Science Education 28:2, 140 - 153. https://www.tandfonline.com/doi/full/10. 1080⁄10691898.2020.1756542
Basil, M. R. K., Huque, S., McConville, K. S., Moisen, G. G., and T. S. Frescino. (2020) Creating Homogeneous Landfire Vegetation Classes for Forest Inventory Applications in the Interior West. Gen. Tech. Rep. US. Department of Agriculture, Forest Service, Southern Research Station. https://www.fs.usda.gov/treesearch/pubs/60966
Rintoul, M. A., Maebius, S, Alvarado, E, Lloyd-Damnjanovic, A., Toyohara, M., McConville, K. S., Moisen, G. G., and T. S. Frescino. (2020) An Alternative Post-Stratification Scheme toDecrease Variance of Forest Attributes in the Interior West. Gen. Tech. Rep. US. Department of Agriculture, Forest Service, Southern Research Station. https://www.fs.usda.gov/treesearch/pubs/60966
McConville, K. S., Moisen, G. G., and T. S. Frescino. (2020) A Tutorial on Model-Assisted Estimation with Application to Forest Inventory. Forests, 11:2, 244, https://doi.org/10.3390/f11020244
McConville, K. S. and D. Toth. (2019) Automated Selection of Post-Strata using a Model-Assisted Regression Tree Estimator. Scandinavian Journal of Statistics 42:2, 389-413. https://doi.org/10.1111/sjos.12356
McConville, K. S., Stokes, L., and M., Gray. (2018). Accumulating Evidence of the Impact of Voter ID Laws: Student Engagement in the Political Process. Statistics and Public Policy 5:1, 1-8. https://doi.org/10.1080/2330443X.2017.1407721
McConville, K. S., Breidt, F. J., Lee, T. C. M., and G. Moisen (2017). Model-Assisted Survey Regression Estimation with the Lasso. Journal of Survey Statistics and Methodology 5, 131-158. https://doi.org/10.1093/jssam/smw041
McConville, K. S. and F. J. Breidt (2013). Survey Design Asymptotics of the Model-Assisted Penalised Spline Regression Estimator. Journal of Nonparametric Statistics 25, 745-763. https://doi.org/10.1080/10485252.2013.780057
Ayala, J., Corbin, P., McConville, K., Colonius, F., Kliemann, W., and J. Peters (2006). Morse Decompostion, Attractors, and Chain Recurrence. Proyecciones Journal of Mathematics 25, 79-109.
Software
Kelly McConville, Josh Yamamoto, Becky Tang, George Zhu, Shirley Cheung, and Sida Li (2018). mase: Model-Assisted Survey Estimation. R package version 0.1.5.1. https://cran.r-project.org/package=mase
Kelly McConville and Isabelle Caldwell (2020). pdxTrees: Data Package of Portland, Oregon Trees. R package version 0.4.0. https://CRAN.R-project.org/package=pdxTrees