Advanced search
Publication Cover
Statistical Theory and Related Fields Volume 6, 2022 - Issue 3
Submit an article Journal homepage
1,629
Views
1
CrossRef citations to date
0
Altmetric
Review Article

A selective review of statistical methods using calibration information from similar studies

Jing Qina National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USAView further author information
,
Yukun Liub KLATASDS – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of ChinaCorrespondence[email protected]
View further author information
&
Pengfei Lic Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, CanadaView further author information
Pages 175-190 | Received 04 Jan 2021, Accepted 10 Jan 2022, Published online: 17 Feb 2022

References

  • Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models and issues in data stream systems. In Proceedings of the 21 ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (pp. 1–16). ACM.
  • 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
  • Braverman, M., Garg, A., Ma, T., Nguyen, H., & Woodruff, D. (2016). Communication lower bounds for statistical estimation problems via a distributed data processing inequality. In Proceedings of the 48th annual ACM symposium on theory of computing (pp. 1011–1020). ACM.
  • Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. (2009). Introduction to meta-analysis. Wiley.
  • 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
  • Chaudhuri, S., Handcock, M. S., & Rendall, M. S. (2008). Generalized linear models incorporating population level information: an empirical likelihood based approach. Journal of the Royal Statistical Society: Series B, 70(2), 311–328. https://doi.org/10.1111/rssb.2008.70.issue-2
  • Chen, J., & Qin, J. (1993). Empirical likelihood estimation for finite populations and the effective usage of auxiliary information. Biometrika, 80(1), 107–116. https://doi.org/10.1093/biomet/80.1.107
  • Chen, J., Sitter, R., & Wu, C. (2002). Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys. Biometrika, 89(1), 230–237. https://doi.org/10.1093/biomet/89.1.230
  • Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.
  • Dersimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7(3), 177–188. https://doi.org/10.1016/0197-2456(86)90046-2
  • Duan, R., Ning, Y., & Chen, Y. (2020). Heterogeneity-aware and communication-efficient distributed statistical inference. arXiv:1912.09623v1.
  • Duchi, J., Jordan, M., Wainwright, M., & Zhang, Y. (2015). Optimality guarantees for distributed statistical estimation. arXiv:1405.0782.
  • Han, P., & Lawless, J. (2016). Comment. Journal of the American Statistical Association, 111(513), 118–121. https://doi.org/10.1080/01621459.2016.1149399
  • Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054. https://doi.org/10.2307/1912775
  • Hartely, H. O., & Rao, J. N. K. (1968). A new estimation theory for sample surveys. Biometrika, 55(3), 547–557. https://doi.org/10.1093/biomet/55.3.547
  • Imbens, G., & Lancaster, T. (1994). Combining micro and macro data in microeconometric models. Review of Economic Studies, 61(4), 655–680. https://doi.org/10.2307/2297913
  • Jordan, M. I., Lee, J. D., & Yang, Y. (2019). Communication-efficient distribution statistical inference. Journal of the American Statistical Association, 114(526), 668–681. https://doi.org/10.1080/01621459.2018.1429274
  • Lee, J., Liu, Q., Sun, Y., & Taylor, J. (2017). Communication-efficient sparse regression. Journal of Machine Learning Research, 18, 1–30. http://jmlr.org/papers/v18/16-002.html
  • Lin, D. Y., & Zeng, D. (2010). On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika, 97(2), 321–332. https://doi.org/10.1093/biomet/asq006
  • Luo, L. (2020). Renewable estimation and incremental inference in generalized linear models with streaming data sets. Journal of the Royal Statistical Society, Series B, 82(1), 69–97. https://doi.org/10.1111/rssb.12352
  • Neiswanger, W., Wang, C., & Xing, E. (2015). Asymptotically exact, embarrassingly parallel MCMC. In Proceedings of the 30th conference on uncertainty in artificial intelligence (pp. 623–632). AUAI Press.
  • Nguyen, T. D., Shih, M. H., Srivastava, D., Tirthapura, S., & Xu, B. (2021). Stratified random sampling from streaming and stored data. Distributed and Parallel Databases, 39(3), 665–710. https://doi.org/10.1007/s10619-020-07315-w
  • Owen, A. B. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika, 75(2), 237–249. https://doi.org/10.1093/biomet/75.2.237
  • Owen, A. B. (1990). Empirical likelihood ratio confidence regions. Annals of Statistics, 18(1), 90–120. https://doi.org/10.1214/aos/1176347494
  • Owen, A. B. (2001). Empirical likelihood. CRC.
  • Qin, J. (2000). Combining parametric and empirical likelihoods. Biometrika, 87(2), 484–490. https://doi.org/10.1093/biomet/87.2.484
  • Qin, J. (2017). Biased sampling, over-identified parameter problems and beyond. Springer.
  • Qin, J., & Lawless, J. (1994). Empirical likelihood and general equations. Annals of Statistics, 22(1), 300–325. https://doi.org/10.1214/aos/1176325370
  • Qin, J., Zhang, H., Li, P., Albanes, D., & Yu, K. (2015). Using covariate specific disease prevalence information to increase the power of case-control study. Biometrika, 102(1), 169–180. https://doi.org/10.1093/biomet/asu048
  • Susanne, M. S. (2007). Point estimation with exponentially tilted empirical likelihood. Annals of Statistics, 35(2), 634–672. https://doi.org/10.1214/009053606000001208
  • Tian, L., & Gu, Q. (2016). Communication-efficient distributed sparse linear discriminant analysis. arXiv:1610.04798.
  • van de Geer, S., Buhlmann, P., Ritov, Y., & Dezeure, R. (2014). On asymptotically optimal confidence regions and tests for high dimensional models. Annals of Statistics, 42(3), 1166–1202. https://doi.org/10.1214/14-AOS1221
  • van de Vaart, V. W. (2000). Asymptotic statistics. Cambridge University Press.
  • Wang, X., & Dunson, D. (2015). Parallelizing MCMC via Weierstrass sampler. arXiv:1312.4605.
  • Wang, J., Kolar, M., Srebro, N., & Zhang, T. (2017). Efficient distributed learning with sparsity. In Proceedings of the 34th international conference on machine learning, Sydney, Australia, PMLR 70 (pp. 3636–3645).
  • Wu, C., & Sitter, R. R. (2001). A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association, 96(453), 185–193. https://doi.org/10.1198/016214501750333054
  • Wu, C., & Thompson, M. E. (2020). Sampling theory and practice. Springer.
  • Zeng, D. & Lin, D. Y. (2015). On random-effects meta-analysis. Biometrika, 102(2), 281–294.
  • Zhang, Y., Duchi, J., & Wainwright, M. (2013). Communication-efficient algorithms for statistical optimization. Journal of Machine Learning Research, 14, 3321–3363.
  • Zhang, H., Deng, L., Schiffman, M., Qin, J., & Yu, K. (2020). Generalized integration model for improved statistical inference by leveraging external summary data. Biometrika, 107(3), 689–703. https://doi.org/10.1093/biomet/asaa014

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.