Jean Opsomer

Vice President

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 serves on 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

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. and Opsomer J.D. (2017).

Model-assisted survey estimation with modern prediction techniques.

Statistical Science, 32(2), 16.

DOI

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

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

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

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

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).

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

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

Source

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)