References
- Back, K., & Brown, D. P. (1992). GMM, maximum likelihood, and nonparametric efficiency. Economics Letters, 39(1), 23–28. https://doi.org/10.1016/0165-1765(92)90095-G
- Chen, Y., Li, P., & Wu, C. (2020). Doubly robust inference with non-probability survey samples. Journal of the American Statistical Association, 115(532), 2011–2021. https://doi.org/10.1080/01621459.2019.1677241
- Chen, Z., Ning, J., Shen, Y., & Qin, J. (2021). Combining primary cohort data with external aggregate information without assuming comparability. Biometrics, 77(3), 1024–1036. https://doi.org/10.1111/biom.v77.3
- Efron, B. (2020). Prediction, estimation and attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613
- Molanes Lopez, E. M., Van Keilegom, I., & Veraverbeke, N. (2009). Empirical likelihood for non-smooth criterion functions. Scandinavian Journal of Statistics, 36(3), 413–432. https://doi.org/10.1111/sjos.2009.36.issue-3
- Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., & Lawrence, N. D. (2009). Dataset shift in machine learning. MIT Press.
- Sheng, Y., Sun, Y. F., Huang, C. Y., & Kim, M.-K. (2021). Synthesizing external aggregated information in the presence of population heterogeneity: a penalized empirical likelihood approach. Biometrics, 78(2), 679–690. https://doi.org/10.1111/biom.v78.2
- Taylor, J. M. G., Choi, K., & Han, P. (2022). Data integration – exploiting ratios of parameter estimates from a reduced external model. Biometrika. https://doi.org/10.1093/biomet/asac022
- Xie, M. G., & Zheng, Z. (2020). Discussion of professor Bradley efron's article on ‘prediction, estimation, and attribution’. Journal of the American Statistical Association, 115(530), 667–671. https://doi.org/10.1080/01621459.2020.1762614
- Zhai, Y., & Han, P. (2022). Data integration with oracle use of external information from heterogeneous populations. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2022.2050248