SMS scnews item created by John Ormerod at Mon 4 Aug 2014 0929
Type: Seminar
Distribution: World
Expiry: 9 Aug 2014
Calendar1: 8 Aug 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod4.pc (assumed)

# Statistics Seminar: Jingjing Wu (Calgary) -- An Efficient and Robust Estimation Based on Profiling


The successful application of the Hellinger distance approach to fully
parametric models is well known. The corresponding optimal estimators,
known as minimum Hellinger distance (MHD) estimators, are efficient
and have excellent robustness properties (Beran, 1977). This combination
of efficiency and robustness makes MHD estimators appealing in practice.
However, their application to semiparametric statistical models, which
have a nuisance parameter (typically of infinite dimension), has not
been fully studied. In this talk, we investigate a methodology to
extend the MHD approach to general semiparametric models. We introduce
the profile Hellinger distance and use it to construct a minimum profile
Hellinger distance (MPHD) estimator of the finite-dimensional parameter
of interest. This approach is analogous in some sense to the profile
likelihood approach. We investigate the asymptotic properties such as
the asymptotic normality, efficiency, and adaptivity of the proposed
estimator. We also investigate its robustness properties. We present its
small-sample properties using a Monte Carlo study.


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