Robust estimators for generalized linear models with a dispersion parameter

TL;DR

This paper introduces a three-step robust estimation procedure for generalized linear models with a dispersion parameter, combining rank correlation, MT-estimators, and conditional maximum likelihood to achieve robustness and efficiency.

Contribution

The paper presents a novel three-step estimator that is both highly robust and asymptotically efficient for GLMs with dispersion parameters, including negative binomial models.

Findings

Estimator is robust against outliers.

Asymptotically efficient as maximum likelihood estimator.

Effective in negative binomial regression models.

Abstract

Highly robust and efficient estimators for the generalized linear model with a dispersion parameter are proposed. The estimators are based on three steps. In the first step the maximum rank correlation estimator is used to consistently estimate the slopes up to a scale factor. In the second step, the scale factor, the intercept, and the dispersion parameter are consistently estimated using a MT-estimator of a simple regression model. The combined estimator is highly robust but inefficient. Then, randomized quantile residuals based on the initial estimators are used to detect outliers to be rejected and to define a set S of observations to be retained. Finally, a conditional maximum likelihood (CML) estimator given the observations in S is computed. We show that, under the model, S tends to the complete sample for increasing sample size. Therefore, the CML tends to the unconditional maximum likelihood estimator. It is therefore highly efficient, while maintaining the high degree of robustness of the initial estimator. The case of the negative binomial regression model is studied in detail.