Biased sampling models
Greasing non-squeaky wheels
August 27, 2019 — August 27, 2019
Data where your collection process is biased. Can we fix it? Not always.
Connected to hierarchical models, survey estimation as applied in psephology etc., post stratification.
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1 References
Bareinboim, Tian, and Pearl. 2014. “Recovering from Selection Bias in Causal and Statistical Inference.” In AAAI.
Broockman, Kalla, and Sekhon. 2016. “The Design of Field Experiments With Survey Outcomes: A Framework for Selecting More Efficient, Robust, and Ethical Designs.” SSRN Scholarly Paper ID 2742869.
Copas, and Li. 1997. “Inference for Non-Random Samples.” Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Gao, Kennedy, Simpson, et al. 2019. “Improving Multilevel Regression and Poststratification with Structured Priors.” arXiv:1908.06716 [Stat].
Kennedy, and Gelman. 2019. “Know Your Population and Know Your Model: Using Model-Based Regression and Poststratification to Generalize Findings Beyond the Observed Sample.” arXiv:1906.11323 [Stat].
Kohler, Kreuter, and Stuart. 2019. “Nonprobability Sampling and Causal Analysis.” Annual Review of Statistics and Its Application.
Mohan, Pearl, and Tian. 2013. “Graphical Models for Inference with Missing Data.” In Advances in Neural Information Processing Systems.
Pfeffermann. 1993a. “Modeling Survey Data.” International Statistical Review.
———. 1993b. “The Role of Sampling Weights When Modeling Survey Data.” International Statistical Review / Revue Internationale de Statistique.
Pfeffermann, Krieger, and Rinott. 1998. “Parametric Distributions of Complex Survey Data Under Informative Probability Sampling.” Statistica Sinica.
Rao. 2006. “Interplay Between Sample Survey Theory and Practice: An Appraisal.” Survey Methodology.
Rao, and Molina. 2015. Small Area Estimation.
Wang, Rothschild, Goel, et al. 2015. “Forecasting Elections with Non-Representative Polls.” International Journal of Forecasting.