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

Michael Sverchkov

Michael Sverchkov

Michael (Michail) Sverchkov, Ph.D.

Research Mathematical Statistician, Office of Survey Methods Research
 202-691-7374
Education:
  • Hebrew University of Jerusalem, 2002, PhD, Statistics
  • Moscow State Lomonosov University, 1989, PhD, Mathematics
Fields of Interest:
  • Analysis of complex sample surveys, in particular, informative sample and non-response, machine learning, non-parametric and semi-parametric estimation, optimization, regression and/or calibration, small area estimation, variance estimation in complex surveys
  • Seasonal adjustment, time series analysis
  • Statistical computing
  • Queuing theory, stochastic processes
Professional Experience:

  • July 2001 to present – Research Mathematical statistician, Office of Survey Methods Research, Bureau of Labor Statistics (2001 – 2013 under contract).

  • Fall 2008 – Adjunct Professor/Lecturer, Department of Mathematics, University of Maryland College Park 

  • 1993-2000 – Teaching assistant, Research assistant, Dept. Statistics, Hebrew University

  • Summer 1997, 1998 and 1999 - Visiting research associate, Department of Social Statistics, University of Southampton, U.K.

  • Spring 1999 - Visiting research associate, Department of Mathematics and Statistics, University of Nebraska, Lincoln, U.S.A.

  • 1990-1993 - Senior member of scientific staff, ‘Kontinent' (small joint venture company), Moscow

  • 1988-1990 - Member of scientific staff, Moscow Steel and Alloys Institute, Moscow

  • 1983-1985 – Engineer, Krasnogorsk Mechanical Factory, Krasnogorsk

Selected Publications and Working Papers:

Books:

  • Kedem, B., de Oliveira, V., and Sverchkov, M. (2017). Statistical Data Fusion. World Scientific, 186 pages

Articles in Refereed Journals:

  • Pfeffermann, D., Sverchkov, M., Tiller, R., and Liu, L. (2020). Model-based small area estimation with no samples within the areas, by benchmarking to marginal cross-sectional and time-series estimates, Statistical Theory and Related Fields, 4, pp. 28-42

  • Pfeffermann, D., and Sverchkov, M. (2019). Multivariate small area estimation under nonignorable nonresponse, Statistical Theory and Related Fields, 3, pp. 213-223

  • Sverchkov, M., and Pfeffermann, D.  (2018). Small area estimation under informative sampling and not missing at random non-response. Journal of Royal Statistical Society, ser. A, 181, Part 4, pp. 981–1008

  • Pfeffermann, D., and Sverchkov, M. (2014). Estimation of mean square error of X-11-ARIMA and other estimators of time series components. Journal of Official Statistics, 30, No.4, pp. 811 - 838

  • Pfeffermann, D., and Sverchkov, M.  (2007). Small area estimation under informative probability sampling of areas and within the selected areas. Journal of the American Statistical Association, 102, No. 480, Theory and Methods, pp. 1427-1439

  • Pfeffermann, D. and Sverchkov, M. (2005). Small area estimation under informative sampling, Statistics in Transition, 7, No. 3, pp. 675-684

  • Sverchkov, M., and Pfeffermann, D. (2004). Prediction of finite population totals based on the sample distribution,  Survey Methodology, 30, No.1, pp. 79-92

  • Pfeffermann, D. and Sverchkov, M. (1999). Parametric and semi-parametric estimation of regression models fitted to survey data, Sankhya B,  61, Pt.1, 166 – 186

  • Kella, O. and Sverchkov, M. (1994). On concavity of the mean function and stochastic ordering for reflected processes with stationary increments.  Journal of Applied Probability, 31, No.4, 1140 - 1142

  • Sverchkov, M. and Rykov, V. (1993). On coupling of stochastic processes with embedded point processes.  Special issue on coupling and regeneration, Acta Applicandae Mathematica, 34, No.1

  • Sverchkov, M. (1993). On wide-sense regeneration,  Lecture Notes in Mathematics, 1546   

  • Sverchkov, M. and Smirnov, S. N. (1990). Maximal coupling of D-valued processes,  Soviet Mathematics-Doklady, 41, No. 2

  • Sverchkov, M. and Smirnov, S. N. (1989). On one representation of supermartingales,  Moscow University Bulletin, Computational  Mathematics and Cybernetics, No.3

  • Sverchkov, M. (1984). On nongeometric ergodicity of regenerative phenomena, Vestnik Moskovskogo Universiteta, ser. comput. math. and cybern., No.2 (in Russian)

Chapters in Books:

    • Scott, S., Sverchkov, M., and Pfeffermann, D. (2012) Estimating variance in X-11 seasonal adjustment. Chapter 8 in Bell, R., Holan, S. H., McElroy, T. S. (eds.) Economic Time Series: Modeling and Seasonality, CRC Press, pp. 185 – 210, 4, pp. 28-42

    • Pfeffermann, D. and  Sverchkov, M. (2009). Inference under informative sampling. Chapter 39 in D. Pfeffermann and C. R. Rao (eds.),  Handbook of Statistics. No. 29B, Sample Surveys: Inference and Analysis, pp. 455-487

    • Chambers, R. L., Dorfman, A. H, and Sverchkov M. Yu. (2003). Nonparametric regression with complex survey data. Chapter 11 in R. L. Chambers and C. Skinner (eds.),  Analysis of Survey Data,  Chichester: Wiley , pp.151-174

    •  Pfeffermann, D. and Sverchkov, M. (2003). Fitting generalized linear models under informative sampling. Chapter 12 in R. L. Chambers and C. Skinner (eds.),  Analysis of Survey Data,  Chichester: Wiley , pp. 175 – 195

 

 

Last Modified Date: April 19, 2023