A Comparison of related density-based minimum divergence estimators

146 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation (2012, background): “Although there seems to be no universal way for this (Basu et al. 1998; Jones et al. 2001; Fujisawa and Eguchi 2008), a practical approach would be to use crossvalidation over a fixed divergence such as the squared distance.”
Robust estimation in the normal mixture model (2006, background): “The convergence of the asymptotic relative efficiency is of the order O(β) (Jones et al., 2001), more precisely, V ar( √ nb θβ) = V ar(√nb θ0) + O(β) as n goes to infinity and β goes to zero, and we will show later that a bound of the grosserror-sensitivity is of the order O(1/β).”
Robust parameter estimation with a small bias against heavy contamination (2008, result): “[6] compared the above two types of robust parameter estimation in more detail and proposed an extended class of divergence.”
Testing statistical hypotheses based on the density power divergence (2012, unknown): “See Basu et al. (1998) and Jones et al. (2001) for more details.”
Choosing a robustness tuning parameter (2005, background): “2 , the density power divergence is a particularly appealing special case of the Bregman divergence [ e.”
The Focused Information Criterion (2003, methodology): “One may generalize our results to the case of robust M estimators and to the minimum density divergence estimators of Basu et al. (1998) and Jones et al. (2001).”