Review Articles

Summaries of three keynote lectures at the SAE – 2018

Kai Tan ,

School of Statistics, East China Normal University, Shanghai, People's Republic of China

Lyu Ni

School of Statistics, East China Normal University, Shanghai, People's Republic of China

Pages 215-218 | Received 13 Sep. 2018, Accepted 16 Sep. 2018, Published online: 24 Sep. 2018,
  • Abstract
  • Full Article
  • References
  • Citations


Three keynote lectures are presented at the conference of Small Area Estimation and Other Topics of Current Interest in Surveys, Official Statistics and General Statistics (SAE 2018), an international conference held between June 16 and 18 at East China Normal University, Shanghai, China. The speakers of these lectures are world famous statistics professors, James O. Berger, J. N. K. Rao and Malay Ghosh. The lectures mainly review the previous studies and present the pioneering results covering Bayesian statistics, small area estimation, shrinkage priors, etc.


  1. Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global–local shrinkage priors. Journal of Multivariate Analysis167, 157–170. doi: 10.1016/j.jmva.2018.04.010 [Google Scholar]
  2. Berger, J. O., Strawderman, W., & Tang, D. J. (2005). Posterior propriety and admissibility of hyperpriors in normal hierarchical models. The Annals of Statistics33(2), 606–649. doi: 10.1214/009053605000000075 [Google Scholar]
  3. Carvalho, C. M., Ploson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika97(2), 465–480. doi: 10.1093/biomet/asq017 [Google Scholar]
  4. Chen, L., & Huang, J. Z. (2012). Sparse reduced-rank regression for simultaneous dimension reduction and variable selection. Journal of the American Statistical Association107(500), 1533–1545. doi: 10.1080/01621459.2012.734178 [Taylor & Francis Online], [Google Scholar]
  5. Chun, H., & Kele, S. (2010). Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology)72(1), 3–25. doi: 10.1111/j.1467-9868.2009.00723.x [Web of Science ®], [Google Scholar]
  6. Friedman, J., Hastie, T., & Tishirani, R. J. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software33(01), 1–22. doi: 10.18637/jss.v033.i01 [Google Scholar]
  7. Hoff, P. D. (2009). Multiplicative latent factor models for description and prediction of social networks. Computational and Mathematical Organization Theory15(4), 261. doi: 10.1007/s10588-008-9040-4 [Google Scholar]
  8. Li, Y., Nan, B., & Zhu, J. (2015). Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics71(2), 354–363. doi: 10.1111/biom.12292 [Google Scholar]
  9. Liquet, B., Mengersen, K., Pettitt, A. N., & Sutton, M. (2017). Bayesian variable selection regression of multivariate responses for group data. Bayesian Analysis12(4), 1039–1067. doi: 10.1214/17-BA1081 [Google Scholar]
  10. Park, T., & Casella, G. (2008). The Bayesian lasso. Journal of the American Statistical Association103(482), 681–686. doi: 10.1198/016214508000000337 [Taylor & Francis Online], [Google Scholar]
  11. Pfeffermann, D. (1991). Estimation and seasonal adjustment of population means using data from repeated surveys. Journal of Business & Economic Statistics9(2), 163–175. [Taylor & Francis Online], [Google Scholar]
  12. Pfeffermann, D., & Correa, S. (2012). Empirical bootstrap bias correction and estimation of prediction mean square error in small area estimation. Biometrika99(2), 457–472. doi: 10.1093/biomet/ass010 [Google Scholar]
  13. Pfeffermann, D., & Nathan, G. (1981). Regression analysis of data from a cluster sample. Journal of the American Statistical Association76(375), 681–689. doi: 10.1080/01621459.1981.10477704 [Taylor & Francis Online], [Google Scholar]
  14. Pfeffermann, D., Skinner, C. J., Holmes, D. J., Goldstein, H., & Rasbash, J. (1998). Weighting for unequal selection probabilities in multilevel models. Journal of the Royal Statistical Society: Series B (Statistical Methodology)60(1), 23–40. doi: 10.1111/1467-9868.00106 [Google Scholar]
  15. Pfeffermann, D., & Sverchkov, M. (2007). Small-area estimation under informative probability sampling of areas and within the selected areas. Journal of the American Statistical Association102(480), 1427–1439. doi: 10.1198/016214507000001094 [Taylor & Francis Online], [Google Scholar]