Discussion of “Probability vs. Nonprobability Sampling: From the Birth of Survey Sampling to the Present Day” by Graham Kalton

• Authors: Julie Gershunskaya , Partha Lahiri

Abstracts

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• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2023
• Volume: 24
• Issue: 3
• Pages: 31-37

Dates

published

2023

Contributors

author

Julie Gershunskaya

• U.S. Bureau of Labor Statistics

• https://orcid.org/000000020096186X

author

Partha Lahiri

• University of Maryland

• https://orcid.org/000000027103545X

References

• Beaumont, J.-F., K. Bosa, A. Brennan, J. Charlebois, and K. Chu (2023). Handling nonprobability samples through inverse probability weighting with an application to statistics canada’s crowdsourcing data. Survey Methodology (accepted in 2023 and expected to appear in 2024).
• Bell, W. R., W. W. Basel, and J. J. Maples (2016). An overview of the US Census Bureau’s small area income and poverty estimates program, pp. 349–378. Wiley Online Library.
• Beresovsky, V. (2019). On application of a response propensity model to estimation from web samples. In ResearchGate.
• Casas-Cordero Valencia, C., J. Encina, and P. Lahiri (2016). Poverty mapping for the Chilean Comunas, pp. 379–404. Wiley Online Library.
• Chen, Y., P. Li, and C. Wu (2020). Doubly robust inference with nonprobability survey samples. Journal of the American Statistical Association 115(532), 2011–2021.
• Elliott, M. R. (2009). Combining data from probability and non-probability samples using pseudo-weights. Survey Practice 2, 813–845.
• Elliott, M. R. and R. Valliant (2017). Inference for Nonprobability Samples. Statistical Science 32(2), 249 – 264.
• Ghosh, M. (2020). Small area estimation: Its evolution in five decades. Statistics in Transition New Series, Special Issue on Statistical Data Integration, 1–67.
• Jiang, J. and P. Lahiri (2006). Mixed model prediction and small area estimation, editor’s invited discussion paper. Test 15, 1–96.
• Kim J. and K. Morikawa (2023). An empirical likelihood approach to reduce selectionbias in voluntary samples. Calcutta Statistical Association Bulletin 35 (to appear).
• National Academies of Sciences, E. and Medicine (2017). Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press.
• Rao, J. N. K. and I. Molina (2015). Small Area Estimation, 2nd Edition. Wiley. Savitsky, T. D., M. R.Williams, J. Gershunskaya, V. Beresovsky, and N. G. Johnson (2022). Methods for combining probability and nonprobability samples under unknown overlaps. https://doi.org/10.48550/arXiv.2208.14541.
• Sen, A. and P. Lahiri (2023). Estimation of finite population proportions for small areas: a statistical data integration approach. https://doi.org/10.48550/arXiv.2305.12336.
• Wang, L., R. Valliant, and Y. Li (2021). Adjusted logistic propensity weighting methods for population inference using nonprobability volunteer-based epidemiologic cohorts. Stat Med. 40(4), 5237–5250.

Identifiers

DOI

10.59170/stattrans-2023-032

Biblioteka Nauki

18105111