References
- Chatterjee, N., Chen, Y. H., Maas, P., & Carroll, R. J. (2016). Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources. Journal of the American Statistical Association, 111(513), 107–117. https://doi.org/10.1080/01621459.2015.1123157
- 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
- Estes, J. P., Mukherjee, B., & Taylor, J. M. G. (2018). Empirical bayes estimation and prediction using summary-level information from external big data sources adjusting for violations of transportability. Statistics in Biosciences, 10(3), 568–586. https://doi.org/10.1007/s12561-018-9217-4
- Gu, T., Taylor, J. M. G., & Mukherjee, B. (2021). A meta-inference framework to integrate multiple external models into a current study. Biostatistics, kxab017. https://doi.org/10.1093/biostatistics/kxab017
- Han, P., & Lawless, J. F. (2016). Discussion of “Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources”. Journal of the American Statistical Association, 111(513), 118–121. https://doi.org/10.1080/01621459.2016.1149399
- Han, P., & Lawless, J. F. (2019). Empirical likelihood estimation using auxiliary summary information with different covariate distributions. Statistica Sinica, 29, 1321–1342.
- Neuhaus, J. M., & Jewell, N. P. (1993). A geometric approach to assess bias due to omitted covariates in generalized linear models. Biometrika, 80(4), 807–815. https://doi.org/10.1093/biomet/80.4.807
- Owen, A. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika, 75(2), 237–249. https://doi.org/10.1093/biomet/75.2.237
- Qin, J. (2000). Combining parametric and empirical likelihoods. Biometrika, 87(2), 484–490. https://doi.org/10.1093/biomet/87.2.484
- Qin, J., & Lawless, J. (1994). Empirical likelihood and general estimating equations. The Annals of Statistics, 22(1), 300–325. https://doi.org/10.1214/aos/1176325370
- Sheng, Y., Sun, Y., Huang, C.-Y, & Kim, M.-O. (2021). Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach. Biometrics. https://doi.org/10.1111/biom.13429
- 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
- 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
- Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429. https://doi.org/10.1198/016214506000000735