A comparison of related density-based minimum divergence estimators

Biometrika

Volumn 88, Issue 3, 2001, Pages 865-873

  Jones, M C ^a   Hjort, Nils Lid ^b   Harris, Ian R ^c   Basu, Ayanendranath ^d  

^a OPEN UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF OSLO   (Norway)

^c SOUTHERN METHODIST UNIVERSITY   (United States)

^d INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

A estimation; Asymptotic relative .efficiency; Divergence; Influence function; Maximum likelihood; Robustness

Indexed keywords

EID: 1142272308     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/88.3.865     Document Type: Article

References (7)

1. BASU, A., HARRIS, I. R., HJORT, N. L. & JONES, M. C. (1998). Robust and efficient estimation by minimising a density power divergence. Biometrika 85, 549-59.
2. CHOI, E., HALL, P. & PRESNELL, B. (2000). Rendering parametric procedures more robust by empirically tilting the model. Biometrika 87, 453-65.
3. CSISZâR, I. (1991). Why least-squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Ann. Statist19, 2032-66.
4. DE ANGELIS, D. & YOUNG, G. A. (1992). Smoothing the bootstrap. Int. Statist. Rev60, 45-56.
5. HAMPEL, F. R., RONCHETTI, E. M., ROUSSEUW, P. J. & STAHEL, W. A. (1986). Robust Statistics: the Approach Based on Influence Functions. New York: Wiley.
6. JONES, L. K. & BYRNE, C. L. (1990). General entropy criteria for inverse problems, with applications to data compression, pattern classification, and cluster analysis. IEEE Trans. Info. Theory 36, 23-30.
7. WINDHAM, M. P. (1995). Robustifying model fitting. J. R. Statist. Soc. B 57, 599-609.