U.S. flag

An official website of the United States government

Department of Labor Logo United States Department of Labor
Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Bureau of Labor Statistics > Office of Survey Methods Research > Home

Vladislav Beresovsky

Vladislav Beresovsky  

Vladislav Beresovsky, Ph.D.

Research Mathematical Statistician Office of Survey Methods Research
202-691-6784
Education:
  • University of Maryland, 2008-2012, credit courses in Mathematical and Survey Statistics
  • Troitsk High Pressure Physics Institute, Moscow region, Russia, Ph.D., 1996, Physics of High Temperature Superconductivity
  • Kyiv State University and Kyiv Institute for Metallophysics, Ukraine, M.S., 1990, Solid State Physics
  • Kyiv State University, Ukraine, B.S., 1988, Physics, Radiophysics, Electronics
Fields of Interest:
  • Design based inferences on national level and model-bases estimation in small areas from complex survey samples
  • Missing data imputation, nonresponse adjustment, generalized calibration
  • Quasi-randomized estimation from nonprobability survey samples
  • Applications of Bayesian estimation and machine learning for inferences from complex survey samples
  • Modern approaches to data visualization, particularly R-Shiny and R-Markdown
Professional Experience:
  • Research Mathematical Statistician, Bureau of Labor Statistics, 2023-present
  • Mathematical Statistician, National Center for Health Statistics, 2011-2023
  • Survey Statistician, National Center for Health Statistics, 2008-2011
  • Statistical Analyst for marketing research, DataLab USA, 2007-2008
  • Software Information Technology Engineer, multiple U.S. Private Companies, 1996-2007
  • Research Associate and Postgraduate Student, Troitsk High Pressure Physics Institute, 1992-1996
  • Research Associate, Moscow Institute for Crystallography, 1991-1992
  • Research Associate, Kyiv Institute for Metallophysics, 1990-1991
Selected Publications and Working Papers:

  • Terrance D. Savitsky, Matthew R. Williams, Julie Gershunskaya, Vladislav Beresovsky, Nels G. Johnson. Methods for Combining Probability and Nonprobability Samples Under Unknown Overlaps. Submitted to the Journal of Survey Statistics and Methodology.

  • Holly H. Fisher, Georgianne T. Hawkins, Marci Hertz, Sarah Sliwa, Vladislav Beresovsky. Student and School Characteristics Associated With COVID-19-Related Learning Decline Among Middle and High School Students in K-12 Schools. Journal of School Health, Volume 92, Issue 11, pp 1027-1039.

  • Lauren M. Rossen, Brady Hamilton, Joyce Abma, Elizabeth Gregory, Vladislav Beresovsky, Adriana Resendez, Anjani Chandra, Joyce Martin. Methods to Estimate Overall and Unintended Pregnancy Rates in the United States. To be published as Vital and Health Statistics Series 2 or 3.

  • Alex Strashny, Vladislav Beresovsky, Susan Schappert, Loredana Santo. Survey weights in the 2018 National Ambulatory Medical Care Survey (NAMCS) adjusted using iterative proportional fitting (IPF). Vital and Health Statistics, Series 2, Number 202 (cdc.gov). 

  • Beresovsky V. On application of a response propensity model to estimation using external and reference surveys. Presented at the invited papers session “State-Of-The-Art Inferential Approaches for Non-Probability Samples”. Joint Statistical Meetings. Denver, CO. 07/31/2019.

  • Beresovsky V. Inferences from Nonrandom Samples with Model-Implied Randomization. Presented at the 2018 Federal Committee on Statistical Methodology (FCSM). Washington, DC.  March 7, 2018.

  • Beresovsky V. Comparing Random and Nonrandom Samples using Model-Implied Randomization. Proceedings of the Federal Committee on Statistical Methodology (FCSM) Research Conference. Washington, DC. 05/2018.

  • Beresovsky V. Variance Estimation and other Inferences from Nonrandom Samples under Model-Implied Randomization. Presented at the SPEED session of the 2018 Joint Statistical Meetings. Vancouver, BC, CA.  August 1, 2018.

  • Beresovsky V. Variance Estimation and other Inferences from Nonrandom Samples under Model-Implied Randomization. Prepared for submission to JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association, 2018.

  • D. D. Ingram, D. J. Malec, D. M. Makuc, D. Kruszon-Moran, R. M. Gindi, M. Albert, V. Beresovsky, B. E. Hamilton, J. Holmes, J. Schiller, and M. Sengupta. National Center for Health Statistics Guidelines for Analysis of Trends, Vital and Health Statistics, Vital and Health Statistics, Series 2, Number 178, February 2018 (cdc.gov).

  • Beresovsky V. Imputation classes as a framework for inferences from non-random samples. Presented at the SPEED session of the 2017 Joint Statistical Meetings. Baltimore, MD.  August 2, 2017.

  • Beresovsky V.  Imputation Classes as a Framework for Inferences from Nonrandom Samples. JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association, 2017, pp 2918-2932.

  • Singh A. (AIR and NCHS), Beresovsky V., Ye C.  Estimation from Purposive Samples with the Aid of Probability Supplements but Without Data on the Study Variable.  Presented at Joint Statistical Meetings. Baltimore, MD.  August 3, 2017.

  • Parker JD, Talih M, Malec DJ, Beresovsky V, Carroll M, Gonzalez JF, Hamilton BE, Ingram DD, Kochanek K, McCarty F, Moriarity C, Shimizu I, Strashny A, Ward BW.  National Center for Health Statistics Data Presentation Standards for Proportions. Vital and Health Statistics, Series 2, Number 175, August 2017.

  • Beresovsky V.  Using official surveys to reduce bias of estimates from nonrandom samples, presented at the SPEED session of the Joint Statistical Meetings.  Chicago, Illinois. August 3, 2016.

  • Beresovsky V.  Using official surveys to reduce bias of estimates from nonrandom samples collected by web surveys. JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association, 2016, pp 1804-1819.

  • He Y, Shimizu I, Schappert S, Xu J, Beresovsky V, Khan D, Valverde R, Schenker N. A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014. Journal of Official Statistics, Vol. 32, No. 1, 2016, pp. 147–164.

  • Beresovsky V.  Sensitivity analysis of bias of estimates from web surveys with nonrandomized panel selection. In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association. 2015.

  • Beresovsky V. Protecting survey design information by combining strata and accounting for the realized sample selection.  Submitted to JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association. 2014. (Has won a Joint Statistical Meeting Poster Award from the Survey Research Methods Section (SRMS).

  • Lewis, T., E. Goldberg, N. Schenker, V. Beresovsky, S. Schappert, S. Decker, N. Sonnenfeld, and I. Shimizu. 2014. “The Relative Impacts of Design Effects and Multiple 21 Imputation on Variance Estimates: A Case Study With the 2008 National Ambulatory Medical Care Survey.” Journal of Official Statistics 30, 2016, pp. 147–161.

  • Beresovsky V, Hsiao, J. Methodological aspects of small area estimation from the National Electronic Health Records Survey (NEHRS).  In FCSM Proceedings, Washington, DC. 2012.

  • Ashman JJ, Beresovsky V. Multiple Chronic Conditions Among US Adults Who Visited Physician Offices: Data from the National Ambulatory Medical Care Survey, 2009. Preventing Chronic Disease, 2013;10:120308. DOI: http://dx.doi.org/10.5888/pcd10.120308

  • Beresovsky V. A search for robust model-based small area estimation methods for the National Ambulatory Medical Care Survey (NAMCS).   Joint Statistical Meetings. San Diego, CA. August, 2012.

  • Beresovsky V.,  Burt C., Parsons V.,  Schenker N. and Mutter R. Application of Hierarchical Bayesian Model with Post stratification for Small-Area Estimation from Complex Survey Data.   Joint Statistical Meetings. Miami, FL. August 2, 2011.   

  • Beresovsky V., Malec D. Effect of model misspecification on small area estimation of proportions from the National Ambulatory Medical Care Survey data. In FCSM Proceedings, Washington, DC. 2011.

  • Beresovsky V., Burt C., Parsons V.,  and Schenker N.   Application of Small-Area Estimation Methods to Emergency Department Data from the National Hospital Ambulatory Medical Care Survey  Conference.   2010 Joint Statistical Meetings. Vancouver,

Last Modified Date: May 26, 2023