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Editorial: Government Advances in Statistical Programming (GASP) 2025
Lisa M. Frehill   Peter B. Meyer †   José Bayoán Santiago Calderón †  

Authors

 
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https://doi.org/10.6339/26-JDS243EDI
Pub. online: 12 August 2026      Type: Editorial      Open accessOpen Access

† The views expressed in this paper are those of the authors and do not necessarily represent the U.S. Bureau of Economic Analysis, the U.S. Department of Commerce, the U.S. Bureau of Labor Statistics, or any other U.S. government agency.

Published
12 August 2026

References

 
Agency for Healthcare Research and Quality (2026). Medical Expenditure Panel Survey online at https://meps.ahrq.gov/mepsweb/. (Accessed 11 July 2026).
 
Belyaeva I, Carino C, Wang LC (2026). Leveraging survey metadata for LLM reasoning via knowledge graphs. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1230
 
Champney TF, Qin H (2026). Maximizing linkage in address data: Spatial, exact, and fuzzy matching. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1236
 
Commission on Evidence-Based Policy (2017). “The Promise of Evidence-Based Policymaking” online at https://www2.census.gov/adrm/fesac/2017-12-15/Abraham-CEP-final-report.pdf (Note: the final report and background papers prepared for the Commission are available online at https://acf.gov/opre/project/commission-evidence-based-policymaking-cep) (Accessed 11 July 2026).
 
Data Science for Federal Statistics Interest Group (2025). Program: Government Advances in Statistical Programming. Online at: https://statspolicy.gov/assets/fcsm/files/docs/gasp/GASP2025_Program.pdf (Accessed 11 July 2026).
 
Elkasabi M, Lewis T, Williams MR (2026). An estimation framework for combining probability and non-probability samples. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1234
 
Federal Committee on Statistical Methodology (2026). Online at https://statspolicy.gov/FCSM/about/ (Accessed 11 July 2026).
 
Foundations for Evidence-Based Policymaking Act of 2018. Public Law 115 – 435. Online at https://www.govinfo.gov/app/details/PLAW-115publ435 (Accessed 11 July 2026).
 
Preiss AJ, Konet A, Chew R, Williams MR, Segarra EA, ..., Savitsky TD (2026). A practical guide to differentially private deep learning using the pseudo posterior mechanism. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1237
 
Rhodes S, Johnson D, Sartore L, Garber S, Miller D, Abreu D (2026). Use of farm equipment machine-logged data to inform crop production statistics. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1235
 
RTI International (2026). National Survey on Drug Use and Health online at https://nsduhweb.rti.org/respweb/homepage.cfm (Accessed 11 July 2026).
 
Saluja R, Sun H, Korkmaz G, Carle J, Hubbard R, ..., Dulaney R (2026). Predicting “Yes”: Machine learning and diverse data to boost respondent cooperation. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1242
 
U.S. Census Bureau (2026). American Community Survey online at https://www.census.gov/programs-surveys/acs/ (Accessed 11 July 2026).
 
Yin X, Xie Y, Yan J, Wang S, Liu PY, ..., Chen MH (2026). Prediction intervals and group variable importance for classification models in university enrollment yield. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1241
 
Williams MR, McGuire FH, Savitsky TD (2026). Uncertainty quantification for multi-level models using the survey-weighted pseudo-posterior. Journal of Data Science, 24(3). https://doi.org/10.6339/26-JDS1238

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2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Journal of data science

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