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2016 | 36 | 1-2 | 43-51
Tytuł artykułu

A note on robust estimation in logistic regression model

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Języki publikacji
EN
Abstrakty
EN
Computationally attractive Fisher consistent robust estimation methods based on adaptive explanatory variables trimming are proposed for the logistic regression model. Results of a Monte Carlo experiment and a real data analysis show its good behavior for moderate sample sizes. The method is applicable when some distributional information about explanatory variables is available.
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Twórcy
  • Wroclaw University
Bibliografia
  • [1] A.M. Bianco and E. Martinez, Robust testing in the logistic regression model, Computational Statistics and Data Analysis 53 (2009), 4095-4105.
  • [2] A.M. Bianco and V.J. Yohai, Robust estimation in the logistic regression model, Lecture Notes in Statistics, Springer Verlag, New York 109 (1996), 17-34.
  • [3] E. Cantoni and E. Ronchetti, Robust inference for generalized linear models, Journal of the American Statistical Association 96 (2001), 1022-1030.
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  • [5] C. Croux, G. Haesbroeck and K. Joossens, Logistic discrimination using robust estimators: An influence function approach, Canadian J. Statist. 36 (2008), 157-174.
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  • [7] D.J. Finney, The estimation from individual records of the relationship between dose and quantal response, Biometrika 34 (1947), 320-334.
  • [8] H.R. Kunsch, L.A. Stefanski and R.J. Carroll, Conditionally Unbiased Bounded Influence Estimation in General Regression Models, with Applications to Generalized Linear Models, J. Amer. Statist. Assoc. 84 (1989), 460-466.
  • [9] C.L. Mallows, On some topics in robustness (Tech. Report, Bell Laboratories, Murray Hill, NY, 1975).
  • [10] S. Morgenthaler, Least-absolute-deviations fits for generalized linear model, Biometrika 79 (1992), 747-754.
  • [11] D. Pregibon, Resistant Fits for some commonly used Logistic Models with Medical Applications, Biometrics 38 (1982), 485-498.
  • [12] L. Stefanski, R. Carroll and D. Ruppert, Optimally bounded score functions for generalized linear models with applications to logistic regression, Biometrika 73 (1986), 413-424.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1180
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