Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Comments on: On active learning methods for manifold data

This is a preview of subscription content, log in to check access.

References

  1. Alquier P, Lounici K (2011) PAC-Bayesian bounds for sparse regression estimation with exponential weights. Electron J Stat 5:127–145

  2. Agostinelli C, Greco L (2013) A weighted strategy to handle likelihood uncertainty in Bayesian inference. Comput Stat 28(1):319–239

  3. Basu A, Harris IR, Hjort NL, Jones MC (1998) Robust and efficient estimation by minimising a density power divergence. Biometrika 85:549–559

  4. Basu A, Shioya H, Park C (2011) Statistical inference: the minimum distance approach. Chapman & Hall, Boca Raton

  5. Ghosh A, Basu A (2016) Robust Bayes estimation using the density power divergence. Ann Inst Stat Math 68(2):413–437

  6. Ghosh A, Basu A (2017) General robust Bayes pseudo-posterior: exponential convergence results with applications. ArXiv Pre-print arXiv:1708.09692 [math.ST]

  7. Gong D, Zhao X, Medioni G (2012) Robust multiple manifolds structure learning. In: Proceedings of the 29th international conference on international conference on machine learning, Omnipress, pp 25–32

  8. Greco L, Racugno W, Ventura L (2008) Robust likelihood functions in Bayesian analysis. J Stat Plan Inference 138:1258–1270

  9. Gruenwald P, van Ommen T (2017) Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it. Bayesian Anal 12:1069–1103

  10. Hampel FR (1974) The influence curve and its role in robust estimation. J Am Stat Assoc 69:383–393

  11. Hooker G, Vidyashankar AN (2014) Bayesian Model Robustness via Disparities. TEST 23(3):556–584

  12. Warwick J, Jones MC (2005) Choosing a robustness tuning parameter. J Stat Comput Simul 75:581–588

Download references

Author information

Correspondence to Abhik Ghosh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This comment refers to the invited paper available at: https://doi.org/10.1007/s11749-019-00694-y

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghosh, A. Comments on: On active learning methods for manifold data. TEST 29, 34–37 (2020). https://doi.org/10.1007/s11749-019-00695-x

Download citation