Jean Opsomer

Vice President; Statistical Fellows Committee Co-Chair

Jean Opsomer, Ph.D., is a senior statistician with extensive experience applying statistical methods to answer research questions. He is responsible for the statistical and survey methodology of several large-scale survey projects.

Dr. Opsomer is an Adjunct Professor in the Department of Mathematics at University of Maryland, College Park. Previously, he was a faculty member in the Departments of Statistics at Colorado State University and Iowa State University. His recent research has focused on the introduction of shape-constrained and nonparametric methods in survey estimation and on several interdisciplinary projects with survey components on a range of topics (higher education, public health, nutrition, employment, fisheries management, methane emissions, forest health, and agricultural erosion). The author or coauthor of 70 peer-reviewed articles, he has introduced a number of influential novel statistical methodologies into survey estimation. His methodological and theoretical work is frequently motivated by questions that arise within federal statistical agencies with which he has long-term collaborations.

Dr. Opsomer is a Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics and an Elected Member of the International Statistical Institute. He is the recipient of the Carver Medal of the Institute of Mathematical Statistics. In recognition of his contributions to the field, Dr. Opsomer has been named a Westat Senior Statistical Fellow and co-chairs the Statistical Fellows Committee, which provides consultation on important survey statistics issues and addresses recent advances in applied statistics.

Education

Ph.D., Operations Research , Cornell University

M.B.A., Finance, University of Chicago

M.S., Management Engineering, Katholieke Universiteit Leuven, Belgium

Areas of Expertise

Statistical Sciences

Selected Publications

Opsomer J., Chen A., Chang W.-Y., and Foley D. (2021).

U.S. employment higher in the private sector than in the education sector for U.S.-trained doctoral scientists and engineers: Findings from the 2019 Survey of Doctorate Recipients.

(NSF 21-319). Alexandria, VA: National Science Foundation.

Source

Piesse A., Opsomer J., Dohrmann S., DiGaetano R., Morganstein D., Taylor K., Carusi C., and Hyland A. (2021).

Longitudinal uses of the Population Assessment of Tobacco and Health Study.

Tobacco Regulatory Science, 7(1), 3-16.

Source | DOI | PubMed

Oliva-Aviles C., Meyer M.C., and Opsomer J.D. (2020).

Estimation and inference of domain means subject to qualitative constraints.

Survey Methodology, 46(2), 145-180.

Source

Harrison S.P., Schoen R., Atcitty D., Fiegener R., Goodhue R., Havens K., House C.C., Johnson R.C., Leger E., Lesser V., Opsomer J.D., Shaw N., Soltis D.E., Swinton S.M., Toth E., and Young S.A. (2020).

Preparing for the need for a supply of native seed.

Ecological Restoration, 38(4), 203-206.

DOI

Meilan-Vila A., Opsomer J.D., Francisco-Fernandez M., and Crujeiras R.M. (2020).

A goodness-of-fit test for regression models with spatially correlated errors.

Test, 29, 728-749.

DOI

Olson K., Smyth J.D., Horwitz R., Keeter S., Lesser V., Marken S., Mathiowetz N.A., McCarthy J.S., O'Brien E., Opsomer J.D., Steiger D., Sterrett D., Su J., Suzer-Gurtekin Z.T., Turakhia C., and Wagner J. (2020).

Transitions from telephone surveys to self-administered and mixed-mode surveys: AAPOR task force report.

Journal of Survey Statistics and Methodology.

DOI

Dohrmann S., Jones M., Kalton G., and Opsomer J. (2019).

A review of the Address Coverage Enhancement Scheme for in-person household surveys.

Proceedings of the 2019 Survey Research Methods Section of the American Statistical Association. (pp. 1454-1463).

Source (PDF)

Erciulescu A.L. and Opsomer J.D. (2019).

A model-based approach to predict employee compensation components.

Joint Statistical Meetings Proceedings. Government Statistics Section. (pp. 1601-1623). Alexandria, VA: American Statistical Association.

Source (PDF)

Oliva-Aviles C., Meyer M.C., and Opsomer J.D. (2019).

Checking validity of monotone domain mean estimators.

Canadian Journal of Statistics, 47, 315-331.

DOI

Ma H., Ogawa T.K., Sminkey T.R., Breidt F.J., Lesser V.M., Opsomer J.D., Foster J.R., and Van Voorhees D.A. (2018).

Pilot surveys to improve monitoring of marine recreational fisheries in Hawaii.

Fisheries Research, 204, 197-208.

DOI

Breidt F.J., Opsomer J.D., and Huang C.M. (2018).

Model-assisted survey estimation with imperfectly matched auxiliary data.

In Predictive econometrics and big data (pp. 21-35). (Studies in Computational Intelligence). New York, NY: Springer.

Source

Yu H., Wang Y., Opsomer J.D., Wang P., and Ponce N. (2018).

A design-based approach to small area estimation using semiparametric generalized linear mixed models.

Journal of the Royal Statistical Society, Series A, 181, 1151-1167.

DOI

De Brabanter K., Cao F., Gijbels I., and Opsomer J.D. (2018).

Local polynomial regression with correlated errors in random design and unknown correlation structure.

Biometrika, 105(3), 681-690.

DOI

Breidt F.J. and Opsomer J.D. (2017).

Model-assisted survey estimation with modern prediction techniques.

Statistical Science, 32(2), 16.

DOI

Hernandez-Stumpfhauser D., Breidt F.J., and Opsomer J.D. (2016).

Variational approximations for selecting hierarchical models of circular data in a small area estimation application.

Statistics in Transition, 17(2), 91-104.

Source

Wu J., Meyer M.C., and Opsomer J.D. (2016).

Survey estimation of domain means that respect natural orderings.

Canadian Journal of Statistics, 44(4), 431-444.

DOI

Opsomer J.D., Breidt F.J., White M., and Li Y. (2016).

Successive difference replication variance estimation in two-phase sampling.

Journal of Survey Statistics and Methodology, 4(1), 43-70.

DOI

Ma H., Ogawa T.K., Breidt F.J., Lesser V.M., Opsomer J.D., Sminkey T.R., Hawkins C., Bagwill A., and Van Voorhees D.A. (2016).

Pilot surveys of shore fishing on Oahu, Hawaii.

Proceedings of the Survey Research Methods Section, American Statistical Association (pp. 2355-2369). Alexandria, VA: American Statistical Association.

Source (PDF)

Breidt F.J., Opsomer J.D., and Sanchez-Borrego I. (2016).

Nonparametric variance estimation under fine stratification: An alternative to collapsed strata.

Journal of the American Statistical Association, 111(514), 822-833.

DOI

Ranalli M.G., Breidt F.J., and Opsomer J.D. (2016).

Nonparametric regression methods for small area estimation.

In M. Pratesi (Ed.), Analysis of poverty data by small area estimation (pp. 187-204). New York, NY: Wiley.

DOI

Hernandez-Stumpfhauser D., Breidt F.J., and Opsomer J.D. (2016).

Hierarchical Bayesian small area estimation for circular data.

Canadian Journal of Statistics, 44(4), 416-430.

DOI

Jean Opsomer

Education

Areas of Expertise

Statistical Sciences

Selected Publications

U.S. employment higher in the private sector than in the education sector for U.S.-trained doctoral scientists and engineers: Findings from the 2019 Survey of Doctorate Recipients.

Longitudinal uses of the Population Assessment of Tobacco and Health Study.

Estimation and inference of domain means subject to qualitative constraints.

Preparing for the need for a supply of native seed.

A goodness-of-fit test for regression models with spatially correlated errors.

Transitions from telephone surveys to self-administered and mixed-mode surveys: AAPOR task force report.

A review of the Address Coverage Enhancement Scheme for in-person household surveys.

A model-based approach to predict employee compensation components.

Checking validity of monotone domain mean estimators.

Pilot surveys to improve monitoring of marine recreational fisheries in Hawaii.

Model-assisted survey estimation with imperfectly matched auxiliary data.

A design-based approach to small area estimation using semiparametric generalized linear mixed models.

Local polynomial regression with correlated errors in random design and unknown correlation structure.

Model-assisted survey estimation with modern prediction techniques.

Variational approximations for selecting hierarchical models of circular data in a small area estimation application.

Survey estimation of domain means that respect natural orderings.

Successive difference replication variance estimation in two-phase sampling.

Pilot surveys of shore fishing on Oahu, Hawaii.

Nonparametric variance estimation under fine stratification: An alternative to collapsed strata.

Nonparametric regression methods for small area estimation.

Hierarchical Bayesian small area estimation for circular data.

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You’ll be in great company.