Main function

[BERENZ]

TBA

[LukaszChrostowski]

Package content

1. Mass imputation -- families: gaussian, binomial, gamma, inverse.gaussian, poisson,`` quasi*and MASS::negative.binomial.

• [ ] Estimator: using model based approach (stats::glmand MASS::glm.nb)
• [ ] Estimator: using predictive mean matching (RANN::nn2)
• [ ] Variance estimator: Kim et al. (2021), p. 950.

Literature:

Kim, J. K., Park, S., Chen, Y., & Wu, C. (2021). Combining non-probability and probability survey samples through mass imputation. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184(3), 941–963. https://doi.org/10.1111/rssa.12696

2. Propensity score - binomial() with logit, probit and cloglog links

• [ ] unit-level survey data is available
• [ ] only population totals are available
• [ ] residuals (pearson, deviance)
• [ ] Samples overlap and are independent/dependent

Literature: Chen, Y., Li, P., & Wu, C. (2020). Doubly Robust Inference With Nonprobability Survey Samples. Journal of the American Statistical Association, 115(532), 2011–2021. https://doi.org/10.1080/01621459.2019.1677241

3. Doubly robust

• [ ] standard : Chen, Li and Wu (2020)
• [ ] with minimization of asymptotic bias: Yang, Kim and Rui (2020)

Literature:

Chen, Y., Li, P., & Wu, C. (2020). Doubly Robust Inference With Nonprobability Survey Samples. Journal of the American Statistical Association, 115(532), 2011–2021. https://doi.org/10.1080/01621459.2019.1677241 Kim, J. K., & Wang, Z. (2018). Sampling Techniques for Big Data Analysis. International Statistical Review, 1, 1–15. https://doi.org/10.1111/insr.12290 Yang, S., Kim, J. K., & Rui, S. (2020). Doubly robust inference when combining probability and non-probability samples with high dimensional. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 82(2), 445–465. https://doi.org/10.1111/rssb.12354

4. Inverse Probability Weighted

• [ ] population size is or is not available

[BERENZ]

@LukaszChrostowski close?

[LukaszChrostowski]

@BERENZ yes