Survey statistics: I have been involved in the design and estimation for the National Resources Inventory (NRI) survey, as well as several other surveys conducted by the Center for Survey Statistics and Methodology at Iowa State University. On the methodological side, I have been collaborating with Jay Breidt on several topics in survey estimation, including nonparametric model-assisted estimation techniques, variance estimation, calibration and small area estimation.
Nonparametric regression: my current interests in this area are in the development of penalized spline regression methodology, and on the application of nonparametric regression techniques in survey statistics.
Environmental statistics: I seek to develop advanced statistical tools to increase our understanding of environmental processes as well as the human impact on the environment. My primary areas of application are environmental economics, environmental toxicology and natural resource surveys. I have collaborated with faculty in the Department of Agronomy at Iowa State University on building a daily erosion prediction model for Iowa, and I was involved in a watershed-level agro-ecological experiment to assess the feasibility and impacts of combining native prairie and intensive agriculture in Iowa.
I do not accept funding from organizations active in national security, military, surveillance and similar areas, nor will I collaborate with individuals associated with those. This exclusion reflects a deeply-held belief that the militarization of science and research is detrimental to human progress towards peace and equitable distribution of resources. As a researcher, I consider it my responsibility that my work not cause harm to other people, either directly or indirectly, and as an educator, I strive to demonstrate this commitment in my professional activities.
Wu, J., M.C. Meyer and J.D. Opsomer (2015). "Penalized isotonic regression." Journal of Statistical Planning and Inference, 161, 12-24 (DOI: 10.1016/j.jspi.2014.12.008).
J.C. Wang, J.D. Opsomer and H. Wang (2014). "Bagging non-differentiable estimators in complex surveys." Survey Methodology, 40, 189-209.
I. Sanchez-Borrego, J.D. Opsomer, M. Rueda and A. Arcos (2014). "Nonparametric estimation with mixed data types in survey sampling." Revista Matemática Complutense, 27, 685-700 (DOI: 10.1007/s13163-013-0142-2).
L. Diao, D.D. Smith, G. Datta, T. Maiti and J.D. Opsomer (2014). “Accurate Confidence Interval Estimation of Small Area Parameters under the Fay-Herriot Model.” Scandinavian Journal of Statistics, 41, 497-515 (DOI: 10.1111/sjos.12045).
Wang, H., M.C. Meyer and J.D. Opsomer (2013). "Constrained Spline Regression in the Presence of AR(p) Errors." Journal of Nonparametric Statistics, 25, 809-827 (DOI: 10.1080/10485252.2013.804075).
J.R. Tipton, J.D. Opsomer, G.G. Moisen, G.G. (2013). “Properties of the Endogenous Post-Stratified Estimator Using a Random Forest Model.” Remote Sensing of the Environment, 139,130-137 (DOI 10.1016/j.rse.2013.07.035).
J.D. Opsomer (2013). “Nonparametric regression model.” Encyclopedia of Environmetrics Second Edition, A.-H. El-Shaarawi and W. Piegorsch (eds). John Wiley & Sons Ltd, Chichester, UK, 1798-1811 (DOI: 10.1002/9780470057339.van019.pub2).
M. Dahlke, F.J. Breidt, J.D. Opsomer and I. Van Keilegom (2013). “Nonparametric endogenous post-stratification in surveys.” Statistica Sinica, 23, 189-211 (DOI: 10.5705/ss.2011.272).
J.W. Karl, M.C. Duniway, S.M. Nusser, J.D. Opsomer and R.S. Unnasch (2012). “Using VHR Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations.” Rangeland Ecology and Management, 65, 330-339 (DOI: 10.2111/REM-D-11-00102.1).
J.D. Opsomer, M. Francisco-Fernandez and X. Li (2012). “Variance estimation for systematic sampling designs using nonparametric regression.” Scandinavian Journal of Statistics, 39, 528-542 (DOI: 10.1111/j.1467-9469.2011. 00773.x).
J.D. Opsomer (2011). “Innovations in Survey Sampling Design: Discussion of Three Contributions Presented at the U.S. Census Bureau.” Survey Methodology, 37: 227-231.
G. Kauermann and J.D. Opsomer (2011). Data-driven Selection of the Spline Dimension in Penalized Spline Regression. Biometrika, 98, 225-230.J.C. Wang and J.D. Opsomer (2011). On the asymptotic normality and variance estimation of nondifferentiable survey estimators. Biometrika, 98, 91-106.
J.D. Opsomer and F.J. Breidt (2011). Nonparametric regression using kernel and spline methods. International Encyclopedia of Statistical Science, Miodrag Lovric (editor), Springer, Part 14, 974-977.
J.D. Opsomer and M. Francisco-Fernandez (2010). Finding Local Departures from a Parametric Model Using Nonparametric Regression. Statistical Papers, 51, 69-84.
G. Kauermann, G. Claeskens and J.D. Opsomer (2009). Bootstrapping for Penalized Spline Regression. Journal of Computational and Graphical Statistics, 18, 126-146.
da Silva, D.N. and J.D. Opsomer (2009). Nonparametric propensity weighting for survey nonresponse through local polynomial regression. Survey Methodology, 35, 165-176.
J.D. Opsomer (2009). Alternative approaches to inference from survey data, in Handbook of Statistics - Sample Surveys: Inference and Analysis, Vol. 29B, D. Pfeffermann and C.R. Rao (editors), The Netherlands: North-Holland, 3-10.
Breidt, F.J. and J.D. Opsomer (2009). Nonparametric and semiparametric estimation in complex surveys, in Handbook of Statistics - Sample Surveys: Inference and Analysis, Vol. 29B, D. Pfeffermann and C.R. Rao (editors), The Netherlands: North-Holland, 103-120.
G. Claeskens, T. Krivobokova and J.D. Opsomer (2009). Asymptotic properties of penalized spline estimators. Biometrika, 96, 529-544.
A.A. Johnson, F.J. Breidt and J.D. Opsomer (2008). Estimating distribution functions from survey data using nonparametric regression. Journal of Statistical Theory and Practice, 2, 419-431.
F.J. Breidt and J.D. Opsomer (2008). Endogenous post-stratification in surveys: classifying with a sample-fitted model. Annals of Statistics, 36, 403-427.
J.D. Opsomer, G. Claeskens, M.G. Ranalli, G. Kauermann and F.J. Breidt (2008). Nonparametric small area estimation using penalized spline regression. Journal of the Royal Statistical Society, Series B, 70, 265-286.
Breidt, F.J. and J.D. Opsomer (2007). Discussion of `Struggles with survey weighting and regression modeling’ by A. Gelman. Statistical Science, 22, 168-170.
F.J. Breidt, J.D. Opsomer, A.A. Johnson and M.G. Ranalli (2007). Semiparametric model-assisted estimation for natural resource surveys. Survey Methodology, 33, 35-44.
J.D. Opsomer, F.J. Breidt, G.G. Moisen and G. Kauermann (2007). Model-assisted estimation of forest resources with generalized additive models (with discussion). Journal of the American Statistical Association, 102, 400-416.
D. N da Silva and J.D. Opsomer (2006). A kernel smoothing method to adjust for unit nonresponse in sample surveys. Canadian Journal of Statistics, 34, 563-579.
M. Francisco-Fernandez, M. Jurado-Exposito, J.D. Opsomer and F. Lopez-Granados (2006). A nonparametric analysis of the distribution of Convolvulus arvensis in wheat-sunflower rotations. Environmetrics, 17, 849-860.
R.M. Cruse, D. Flanagan, J. Frankenberger, B.K. Gelder, D. Herzmann, D. James, W. Krajewski, M. Kraszewski, J.M. Laflen, J.D. Opsomer, and D. Todey (2006). Daily estimates of rainfall, water runoff, and soil erosion in Iowa. Journal of Soil and Water Conservation, 61, 191-199.
F.J. Breidt, G. Claeskens and J.D. Opsomer (2005). Model-assisted estimation for complex surveys using penalized splines. Biometrika, 92, 831-846.
M. Francisco-Fernandez and J.D. Opsomer (2005), Smoothing Parameter Selection Methods for Nonparametric Regression with Spatially Correlated Errors. Canadian Journal of Statistics, 33, 279-295.
J.D. Opsomer and C.P. Miller (2005). Selecting the Amount of Smoothing in Nonparametric Regression Estimation for Complex Surveys. Journal of Nonparametric Statistics, 17, 593-611.
P. Hall and J.D. Opsomer (2005). Theory for penalised spline regression. Biometrika, 92, 105-118.
D.N. da Silva and J.D. Opsomer (2004). Properties of the Weighting Cell Estimator under a Nonparametric Response Mechanism. Survey Methodology, 30, 45-55.
M. Francisco-Fernandez, J.D. Opsomer and J. Vilar-Fernandez (2004), A plug-in bandwidth selector for local polynomial regression estimator with correlated errors. Journal of Nonparametric Statistics, 16, 127-151.
G. Kauermann and J.D. Opsomer (2004). Generalized cross-validation for bandwidth selection of backfitting estimators in generalized additive models. Journal of Computational and Graphical Statistics, 13, 66-89.
G. Kauermann and J.D. Opsomer (2003). Local likelihood estimation in generalized additive models. Scandinavian Journal of Statistics, 30, 317-337.
J.D. Opsomer, C. Botts and J.Y. Kim (2003). Small area estimation in a watershed erosion assessment survey. Journal of Agricultural, Biological and Environmental Statistics, 8, 139-152.
J.D. Opsomer, H.H. Jensen and S. Pan (2003). An Evaluation of the USDA Food Security Measure with Generalized Linear Mixed Models. Journal of Nutrition, 133, 421-427.
J.D. Opsomer (2002). Nonparametric regression model. Encyclopedia of Environmetrics, A.H. El-Shaarawi and W.W. Piegorsch (editors), Wiley & Sons, Chichester U.K., Volume 3, 1411-1425.
J.D. Opsomer, Y. Wang and Y. Yang (2001). Nonparametric regression with correlated errors. Statistical Science, 16, 134-153.
F.J. Breidt and J.D. Opsomer (2000). Local polynomial regression estimators in survey sampling. Annals of Statistics, 28, 1026-1053.
J.D. Opsomer (2000). Asymptotic properties of backfitting estimators. Journal of Multivariate Analysis, 73,166-179.
J.D. Opsomer and D. Ruppert (1999). A root-n consistent estimators for semi-parametric additive models, Journal of Computational and Graphical Statistics, 8:715-732.
J.D. Opsomer, D. Ruppert, M.P. Wand, U. Holst and O. Hossjer (1999). Kriging with nonparametric variance function estimation. Biometrics, 55, 704-710.
J.D. Opsomer and S.M. Nusser (1999). Sample designs for watershed assessment. Journal of Agricultural, Biological and Environmental Statistics, 4, 429-442.
J.D. Opsomer and D. Ruppert (1998). A fully automated bandwidth selection method for fitting additive models. Journal of the American Statistical Association, 93, 605-619.
J.D. Opsomer (1997). Nonparametric regression in the presence of correlated errors, in Modelling Longitudinal and Spatially Correlated Data: Methods, Applications and Future Directions, T.G. Gregoire, D.R. Brillinger, P.J. Diggle, E. Russek-Cohen, W.G. Warren and R.D. Wolfinger (editors), Springer, New York, 339-348.
J.D. Opsomer and D. Ruppert (1997). Fitting a bivariate additive model by local polynomial regression. Annals of Statistics, 25, 186-211.
J.D. Opsomer, J. Agras, A. Carpi and G. Rodrigues (1995). An application of locally weighted regression to airborne mercury deposition around an incinerator site. Environmetrics, 6, 205-221.
J.D. Opsomer and J. M. Conrad (1994). An open-access analysis of the Northern Anchovy fishery. Journal of Environmental Economics and Management, 27, 21-37.
Wu, J., M.C. Meyer and J.D. Opsomer (2015). Survey estimators that respect natural orderings. Submitted to Biometrika.
He, Z. and J.D. Opsomer (2015). Local Polynomial Regression with an Ordinal Covariate. To appear in Journal of Nonparametric Statistics.
Ranalli, M.G., F.J. Breidt and J.D. Opsomer (2015). Nonparametric regression methods for small area estimation. To appear in Analysis of poverty data by small area methods, Monica Pratesi (Ed), Wiley.
Zimmerle, D.J., L.L. Williams, T.L. Vaughn, C. Quinn, R. Subramanian, G.P. Duggan, B. Willson, J.D. Opsomer, A. Marchese, D.M. Martinez, A.L. Robinson (2015). “Methane emissions from the natural gas transmission and storage system in the United States.” To appear in Environmental Science & Technology (DOI: 10.1021/acs.est.5b01669).
Wang, Y., P. Wang, H. Yu, N. Ponce and J.D. Opsomer and N. Ponce (2015). “Generating Health Estimates by Zip Code: A Semi-parametric Small Area Estimation Approach Using the California Health Interview Survey.” To appear in American Journal of Public Health.
D. Hernandez-Stumpfhauser, F.J. Breidt and J.D. Opsomer (2015). Variational Approximations for Selecting Hierarchical Models of Circular Data in a Small Area Estimation Application. To appear in Statistics in Transition.
J.D. Opsomer, F.J. Breidt, M. White and Y. Li (2015). Successive Difference Replication Variance Estimation in Two-Phase Sampling. Resubmitted to Journal of Survey Statistics and Methodology.
F.J. Breidt, J.D. Opsomer and I. Sanchez Borrego (2015). Nonparametric Variance Estimation under Fine Stratification: An Alternative to Collapsed Strata. To appear in Journal of the American Statistical Association (DOI: 10.1080/01621459.2015.1058264).
L. You and J.D. Opsomer (2014). Cross-Validation in Penalized Spline Model-Assisted Estimation. Under revision for Scandinavian Journal of Statistics.
J.D. Opsomer, M. Francisco-Fernandez and X. Li (2012). Supporting Materials for "Model-based nonparametric variance estimation for systematic sampling in a forestry survey."
G. Kauermann and J.D. Opsomer (2010). Data-driven Selection of the Spline Dimension in Penalized Spline Regression: Supplementary Materials.
J.Y Kim, F.J. Breidt and J.D. Opsomer (2009). Nonparametric Regression Estimation of Finite Population Totals under Two-Stage Sampling. Technical Report #2009/4, Department of Statistics, Colorado State University.
D.N. da Silva and J.D. Opsomer (2008). Theoretical properties of propensity weighting for survey nonresponse through local polynomial regression. Technical Report #2008/6, Department of Statistics, Colorado State University.
C.P. Miller and J.D. Opsomer (2004). Theorems on Bandwidth Selection for Local Polynomial Regression with Survey Data. Preprint Series #04-18, Department of Statistics, Iowa State University.
J.D. Opsomer and G. Kauermann (2000). Weighted local polynomial regression, weighted additive models and local scoring. Preprint Series #00-7, Department of Statistics, Iowa State University.
I am collaborating with researchers at the UCLA Center for Health Policy Research on the development AskCHIS Neighborhood Edition website, which provides estimates on health-related variables at the zipcode, city, county, and legislative district level in California. The estimates are created using a nonparametric small area estimation method fitted on data from the California Health Interview Survey (CHIS), similar to that proposed in Opsomer et al. (2008).
|National Research Council (2007). “Understanding American Agriculture: Challenges for the Agricultural Resource Management Survey.” National Academies Press, Washington, D.C. I was a member of the NRC Panel to Review USDA's Agricultural Resource Management Survey.|
As part of project described in the Cruse et al. (2006) article above, we created the Iowa Daily Erosion Predictor website, which is updated daily. While not actually implemented online, we also wrote a technical report on a generalized variance function estimator for the daily erosion predictions.
J.D. Opsomer, H.H. Jensen, S.M. Nusser, D. Drignei, Y. Amemiya (2002). Statistical Considerations for the USDA Food Insecurity Index. Publication #02-WP 307, Center for Agricultural and Rural Development, Iowa State University.
J.D. Opsomer, Z. Wu, T. Isenhart, V. Sitzmann, T.J. Jacobsen, J.Y. Kim, C. Botts (2001). Environmental Assessment for the Rathbun Lake Watershed: Sampling Design, Methods and Results. Preprint Series #01-13, Department of Statistics, Iowa State University.
B.F. McQuaid and L. Norfleet (1999). Assessment of two Carolina watersheds using land and stream habitat quality indices. Journal of Soil and Water Conservation, 657-665. See Opsomer and Nusser (1999) reference above for the methodology.
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Last updated: July 27, 2015.