A note on robust estimation in logistic regression model

Tadeusz Bednarski

Discussiones Mathematicae Probability and Statistics (2016)

  • Volume: 36, Issue: 1-2, page 43-51
  • ISSN: 1509-9423

Abstract

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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.

How to cite

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Tadeusz Bednarski. "A note on robust estimation in logistic regression model." Discussiones Mathematicae Probability and Statistics 36.1-2 (2016): 43-51. <http://eudml.org/doc/286945>.

@article{TadeuszBednarski2016,
abstract = {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.},
author = {Tadeusz Bednarski},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {logistic model; robust estimation; proportional hazards; Cox's regression model; Fréchet differentiable von Mises functionals; consistency; asymptotic normality; infinitesimal nonparametric extension of Cox's semiparametric model; simulation study},
language = {eng},
number = {1-2},
pages = {43-51},
title = {A note on robust estimation in logistic regression model},
url = {http://eudml.org/doc/286945},
volume = {36},
year = {2016},
}

TY - JOUR
AU - Tadeusz Bednarski
TI - A note on robust estimation in logistic regression model
JO - Discussiones Mathematicae Probability and Statistics
PY - 2016
VL - 36
IS - 1-2
SP - 43
EP - 51
AB - 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.
LA - eng
KW - logistic model; robust estimation; proportional hazards; Cox's regression model; Fréchet differentiable von Mises functionals; consistency; asymptotic normality; infinitesimal nonparametric extension of Cox's semiparametric model; simulation study
UR - http://eudml.org/doc/286945
ER -

References

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  1. [1] A.M. Bianco and E. Martinez, Robust testing in the logistic regression model, Computational Statistics and Data Analysis 53 (2009), 4095-4105. Zbl05689161
  2. [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. Zbl0839.62030
  3. [3] E. Cantoni and E. Ronchetti, Robust inference for generalized linear models, Journal of the American Statistical Association 96 (2001), 1022-1030. Zbl1072.62610
  4. [4] R.D. Cook and S. Weisberg, Residuals and Influence in Regression (Chapman and Hall, London, 1982). Zbl0564.62054
  5. [5] C. Croux, G. Haesbroeck and K. Joossens, Logistic discrimination using robust estimators: An influence function approach, Canadian J. Statist. 36 (2008), 157-174. Zbl1143.62036
  6. [6] P. Feigl and M. Zelen, Estimation of exponential probabilities with concomitant information, Biometrics 21 (1965), 826-38. 
  7. [7] D.J. Finney, The estimation from individual records of the relationship between dose and quantal response, Biometrika 34 (1947), 320-334. Zbl0036.09701
  8. [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. Zbl0679.62024
  9. [9] C.L. Mallows, On some topics in robustness (Tech. Report, Bell Laboratories, Murray Hill, NY, 1975). 
  10. [10] S. Morgenthaler, Least-absolute-deviations fits for generalized linear model, Biometrika 79 (1992), 747-754. Zbl0850.62562
  11. [11] D. Pregibon, Resistant Fits for some commonly used Logistic Models with Medical Applications, Biometrics 38 (1982), 485-498. 
  12. [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. Zbl0616.62043

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