论文信息 - Robust Estimation in the Logistic Regression Model

Robust Estimation in the Logistic Regression Model

A new class of robust and Fisher-consistent M-estimates for the logistic regression models is introduced. We show that these estimates are consistent and asymptotically normal. Their robustness is studied through the computation of asymptotic bias curves under point-mass contamination for the case when the covariates follow a multivariate normal distribution. We illustrate the behavior of these estimates with two data sets. Finally, we mention some possible extensions of these M-estimates for a multinomial response.

Ana M. Bianco | V. Yohai

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