An official website of the United States government

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.

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

Terrance Savitsky

Terrance Savitsky, Ph.D.

Senior Research Mathematical Statistician, Office of Survey Methods Research

202-691-7382 savitsky.terrance@bls.gov

Education:

Rice University, M.A., 2009, Statistics
Rice University, Ph.D., 2010, Statistics
Cornell University, M.I.L.R, Human Resources
University of Texas at Austin, M.B.A., Corporate Strategy
Rice University, B.S.M.E, Mechanical Engineering

Fields of Interest:

Bayesian model estimation under complex sampling to achieve frequentist consistency and correct uncertainty quantification with respect to the joint distribution governing population generation and the taking of a complex sample.
Bayesian synthetic data estimation for the achievement of a formal privacy guarantee that well-preserves the utility of the closely-held data distribution.
Bayesian functional data models for small domain estimation for time- and spatially-indexed data.
Bayesian nonparametric modeling, including Gaussian processes, continuous random measures and adaptive auto-regressive prior formulations.

Professional Experience:

Senior Research Mathematical Statistician, Bureau of Labor Statistics, 2021 -present
Research Mathematical Statistician, Bureau of Labor Statistics, 2013 - 2021
Associate Statistician, RAND Corporation, 2010- 2013

Selected Publications and Working Papers

Savitsky, T.D., Williams, M.R. 2022. Pseudo Bayesian Mixed Effects Model Estimation Under Informative Sampling, Journal of Official Statistics.
Hu, J., Savitsky, T.D., Williams, M.R., 2022. Private Tabular Survey Data Products through Synthetic Microdata Generation. Journal of Survey Statistics and Methodology.
Savitsky, T.D., Williams, M.R., Hu, J. 2022. Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy, Journal of Machine Learning Research.
Savitsky, T.D., Gershunskaya, J. 2022. Bayesian Small Area Count data Model for Joint Estimation of Point Estimates and Variances. Under Review at: Annals of Applied Statistics.
Savitsky, T.D., Gershunskaya, J. 2022. Bayesian Nonparametric Joint Model for Domain Point Estimates and Variances under Biased Observed Variances, Journal of Survey Statistics and Methodology.
Hu, J., Savitsky, T.D., Williams, M.R., 2021. Risk-efficient Bayesian Data Synthesis for Privacy Protection. Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smab013
Savitsky, T.D. 2021. Bayesian Dependent Functional Mixture Estimation for Area and Timeindexed Data: An Application for the Prediction of Monthly County Employment, Bayesian Analysis.
Hu, J., Savitsky, T.D., 2021. Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys. Under Review at: Annals of Applied Statistics.
Leon Novelo, L.G., Savitsky, T.D., 2021. Fully Bayesian Mixed Effects Model Estimation under Informative Survey Sampling, To Appear in: Journal of Survey Statistics and Methodology.
Gohaniyogi, R., Li, C., Savitsky, T.D., Srivastava, S., 2021. A Divide-and-Conquer Bayesian Approach to Large-Scale Kriging. Submitted to: Statistical Science.
Williams, M.R., Savitsky, T.D., 2020. Bayesian Uncertainty Estimation Under Complex Sampling. International Statistical Review.
Savitsky, T.D., Gershunskaya, J. 2019. Model Based Screening for Robust Estimation in the presence of deviations from linearity in small domain models, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smz004
Williams, M.R., Savitsky, T.D., 2018. Bayesian Estimation Under Informative Sampling with Unattenuated Dependence, Bayesian Analysis, doi:10.1214/18-BA1143, https://projecteuclid.org/euclid.ba/1546570987, 21 pages.
Leon Novelo, L.G., Savitsky, T.D., 2018. Fully Bayesian Estimation Under Informative Sampling, Electronic Journal of Statistics, Volume 13, Number 1, pp. 1608-1645, https://projecteuclid.org/euclid.ejs/1555466479.
Savitsky, T.D., 2018. Bayesian Pseudo Posterior Estimation for Functional Data under Informative Sampling, Under Review at: Electronic Journal of Statistics.
Williams, M.R., Savitsky, T.D., 2018. Bayesian Pairwise Estimation Under Dependent Informative Sampling, Electronic Journal of Statistics, Volume 12, Number 1, pp. 1631-1661, https://projecteuclid.org/euclid.ejs/1527300143.
Savitsky, T.D. 2018. Bayesian Nonparametric Functional Mixture Estimation for Time-Series Data, With Application to Estimation of State Employment Totals, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smy001, 31 pages.
Savitsky, T.D., Srivastava, S. 2018. Scalable Bayesian Inference Under Informative Sampling, Scandinavian Journal of Statistics, Volume 45, pp. 534-556. https://doi:10.1111/sjos.
Savitsky, T.D., 2017. Bayesian Non-Parametric Multiresolution Estimation for the American Community Survey, Annals of Applied Statistics, Volume 10, Number 4, pp. 2157–2181.
Savitsky, T.D., Gershunskaya, J. 2017. Dependent Latent Effects Modeling for Survey Estimation with Application to the Current Employment Statistics Survey, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smx021, 22 pages.
Savitsky, T.D., 2016. Toth, D., Bayesian Estimation Under Informative Sampling, Electronic Journal of Statistics, Volume 10, Number 1, pp. 1677 - 1708, http://projecteuclid.org/euclid.ejs/1468847267.
Savitsky, T.D., 2016. Bayesian Non-Parametric Functional Mixture Estimation for Time-Indexed Functional Data in R, Journal of Statistical Software, Volume 72, Number 2, pp. 1 - 34, https://www.jstatsoft.org/article/view/v072i02 .

Last Modified Date: April 19, 2023