ncn-foreigners/nonprobsvy

Main function

BERENZ opened this issue · 3 comments

BERENZ commented

TBA

LukaszChrostowski commented

Package content

  1. 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

  1. 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

  1. 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

  1. Inverse Probability Weighted
  • population size is or is not available