Research and education are integral to CSSM's mission to advance the field of survey statistics.
In addition to other statistics courses, CSSM faculty teach graduate level and upper-level courses in statistical survey sampling. They are recognized authorities and experts in survey statistics and in related fields such as spatial statistics, missing data, Bayesian statistics, official statistics and machine learning. CSSM professors work on an assortment of cooperative agreements and grants from U.S. federal agencies such as the Department of Agriculture, Department of the Interior, Department of Justice, National Science Foundation, Institute of Health, Census Bureau, as well as collaborating on other projects covering agriculture, conservation, health, forensics, insurance, economics, finance, transportation, international policies and demographics.
The recent research portfolio of CSSM faculty includes:
- methods to increase the output frequency of the National Resources Inventory (NRI) to make it more timely, accessible, and impactful
- use of machine learning and deep learning tools to detect changes in the land cover/use on NRI segments
- mechanisms to incorporate open-source data to enhance the utility and precision of the NRI
- adoption of a new computational framework which utilizes the pretrained ChatGPT style large geo-spatial model developed by IBM/NASA and fine-tune it for specific prediction tasks.
- quantitative methods and geospatial techniques to efficiently sample fragmented terrains such as the checkerboard regions under the jurisdiction of the Bureau of Land Management
CSSM cooperative agreements and research projects cannot be completed and implemented without the active involvement of research assistants. They are chosen from promising PhD students who are interested in survey statistics and have finished taking advanced survey sampling courses. CSSM graduate students are well versed in the theory and practice of survey sampling. They also hone their communication skills by making presentations and participating in weekly discussions at Survey Group seminars, as well as attending academic conferences in the U.S. and abroad.
Research assistants at CSSM have a 100% job placement rate. Upon graduation, they become assistant professors in other universities, statisticians or mathematical statisticians in government agencies such as National Agricultural Statistics Service, quantitative researchers in medical centers, and data scientists all across the private sector from Bank of America and Google to SAS and Westat.
CSSM Faculty Recent Publications (selected)
Berg, E., & Eideh, A. (2024). Small Area Prediction for Exponential Dispersion Families under Informative Sampling. Journal of Survey Statistics and Methodology, smae018.
Berg, E. (2024): Review of the third edition of sampling: design and analysis, Journal of Applied Statistics, DOI: 10.1080/02664763.2024.2346350
Garman, S., Yu, C., Li, Y. (2024), Composite Estimation to Combine Spatially Overlapping Environmental Monitoring Surveys, PLoS ONE 19(3): e0299306. https://doi.org/10.1371/journal.pone.0299306.
Kim, J.K. and Kwon, Y. (2024). Comments on "Exchangeability assumptions in propensity-score based methods for population mean estimation using non-probability samples", Survey Methodology, Volume 50, 57--63.
Qiu, J., Dai, X., & Zhu, Z. (2024). Nonparametric estimation of repeated densities with heterogeneous sample sizes. Journal of the American Statistical
Association, 119(545), 176-188.
Sun, H., Berg, E., & Zhu, Z. (2024). Multivariate small-area estimation for mixed-type response variables with item nonresponse. Journal of Survey Statistics and Methodology, 12(2), 320-342.
Wang, H., J.K. Kim, J. Han, and Y. Lee. (2024). "Robust propensity score weighting estimation under missing at random", Electronic Journal of Statistics, 18, 2687-2720
CSSM Publications on the NRI and Related Surveys (selected)
Yu, C., Li, J., Karl, M. and Krueger, T. (2020), Obtaining a Balanced Area Sample for the Bureau of Land Management Rangeland Survey, Journal of Agricultural, Biological and Environmental Statistics, Vol. 25, No. 2, 250-275. https://link.springer.com/article/10.1007/s13253-020-00392-5
S.M. Nusser. 2013. National Resources Inventory (NRI), US. Encyclopedia of Environmetrics, A.-H. El-Shaarawi and W. Piegorsch (eds), John Wiley & Sons Ltd: Chichester, UK. https://onlinelibrary.wiley.com/doi/10.1002/9780470057339.van004.pub2
Kim, J.K., A. Navarro & W.A.Fuller (2006) Replication variance estimation for two-phase stratified sampling, Journal of American Statistical Association, 101: 312- 320. https://www.jstor.org/stable/30047459
Goebel, J.J. & R.L. Kellogg (2002) Using survey data and modeling to assist the development of agri-environmental policy, Conference on Agricultural and Environmental Statistical Applications in Rome, National Statistical Institute of Italy, Rome, Italy, 695–704.
Fuller, W.A. (1999) Estimation procedures for the United States National Resources Inventory, Proceedings of the Survey Methods Section of the Statistical Society of Canada, 39 – 44.
Breidt, F.J. & Fuller, W.A. (1999). Design of supplemented panel surveys with application to the National Resources Inventory, Journal of Agricultural, Biological, and Environmental Statistics 4, 391–403.
Fuller, W.A. & F.J. Breidt (1998) Estimation for supplemented panels, Sankhya: The Indian Journal of Statistics, 61: 58 – 70.
Nusser, S.M., Breidt, F.J., & Fuller, W.A. (1998). Design and estimation for investigating the dynamics of natural resources, Ecological Applications 8, 234–245.
Goebel, J. J. (1998) The National Resources Inventory and its role in U.S. agriculture, Agricultural Statistics 2000, International Statistical Institute, Voorburg, The Netherlands, 181-192.
Nusser, S.M. & Goebel, J.J. (1997). The National Resources Inventory: a long-term multiresource monitoring program, Ecological and Environmental Statistics 4, 181–204.