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Biometrika 2001 88(3):865-873; doi:10.1093/biomet/88.3.865
© 2001 by Biometrika Trust
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Miscellaneous

A comparison of related density-based minimum divergence estimators

M.C. Jones1, Nils Lid Hjort2, Ian R.Harris3 and Ayanendranath Basu4

1 Department of Statistics, The Open University, Milton Keynes, MK7 6AA, U.Km.c.jones{at}open.ac.uk 2 Department of Mathematics, University of Oslo, P.B.1053 Blindern, N-0316 Oslo, Norway. nils{at}math.uio.no 3 Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275-0332, U.S.A. iharris{at}mail.smu.edu 4 Applied Statistics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Calcutta 700 035, India. ayanbasu{at}isical.ac.in

This paper compares the minimum divergence estimator of Basu et al.(1998) to a competing minimum divergence estimator which turns out to be equivalent to a method proposed from a different perspective by Windham (1995). Both methods can be applied for any parametric model and contain maximum likelihood as a special case. Efficiencies are compared under model conditions, and robustness properties are studied. Overall the two methods are found to perform quite similarly. Some relatively small advantages of the former method over the latter are identified.

Key Words: Asymptotic relative efficiency; Divergence; Influence function; Maximum likelihood; M-estimation; Robustness


Received August 2000. Revised December 2000


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