Mass imputation -- families: gaussian, binomial, Gamma, inverse.gaussian, poisson, quasi* and MASS::negative.binomial.
[ ] Estimator: using model based approach (stats::glm and 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
Version 0.2.0
Propensity score - binomial() with logit, probit and cloglog links
[ ] unit-level survey data is available
[ ] only population totals are available
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
Version 0.3.0
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
The initial version of the package should have:
Version 0.1.0
gaussian
,binomial
,Gamma
,inverse.gaussian
,poisson
,quasi*
andMASS::negative.binomial
.stats::glm
andMASS::glm.nb
)RANN::nn2
)Literature:
Version 0.2.0
binomial()
withlogit
,probit
andcloglog
linksLiterature:
Version 0.3.0
Literature: