Robust statistical inference: Weighted likelihoods or usual M-Estimation?

Communications in Statistics - Theory and Methods

Volumn 25, Issue 11, 1996, Pages 2597-2613

  Markatou, Marianthi ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Bias; Estimating equations; Influence function; Likelihood; Robustness; Weight function

Indexed keywords

EID: 0000530027     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1080/03610929608831858     Document Type: Article

References (10)

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2. Basu, A., Markatou, M. and Lindsay, B.G. (1995). Weighted likelihood estimating equations: The continuous case. Department of Statistics, Columbia University, New York.
3. Besag, J. (1981). "On resistant techniques and statistical analysis," Biometrika, 68, 463-469.
4. Field, C. and Smith, B. (1994). "Robust estimation- a weighted maximum likelihood approach," International Statistical Review, 62, 405-424.
5. Huber (1981). Robust Statistics. John Wiley, New York.
6. Green, P. J. (1984). "Iteratively reweighted least squares for maximum likelihood estimation and some robust and resistant alternatives," Journal of Royal Statistical Society, Series B, 46, 149-192.
7. Lindsay, B. G. (1994). "Efficiency versus robustness: The case of minimum Hellinger distance and related methods," The Annals of Statistics, 22, 1018-1114.
8. Markatou, M., Basu, A. and Lindsay, B. G. (1995). "Weighted likelihood estimating equations: The discrete case with applications to logistic regression," Journal of Statistical Planning and Inference, forthcoming.
9. Pregibon, D. (1982). "Resistant fits for some commonly used logistic models with medical applications," Biometrics, 38, 485-498.
10. Tsou, T-S. and Royall, R. M. (1995). "Robust likelihoods," Journal of the American Statistical Association, 90, 316-320.