Robust and efficient estimation by minimising a density power divergence

Basu, Ayanendranath; Harris, Ian. R; Hjort, Nils L. and Jones, M. C. (1998). Robust and efficient estimation by minimising a density power divergence. Biometrika, 85(3) pp. 549–559.



A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such as minimum Hellinger distance estimation, avoid the use of nonparametric density estimation and associated complications such as bandwidth selection. The proposed class of ‘density power divergences’ is indexed by a single parameter α which controls the trade-off between robustness and efficiency. The methodology affords a robust extension of maximum likelihood estimation for which α = 0. Choices of α near zero afford considerable robustness while retaining efficiency close to that of maximum likelihood.

Viewing alternatives


Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions
No digital document available to download for this item

Item Actions