Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

Pardo, Leandro; Martín, Nirian

doi:10.3390/e23040430

Cited by 1 publication

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Divergences between probability measures are widely used in statistics and data science in order to perform inference in models of various kinds, parametric or semiparametric. Statistical methods based on divergence minimization extend the likelihood paradigm and often have the advantage of providing a trade-off between efficiency and robustness [8][9][10][11]. A general methodology for the estimation and testing of moment condition models was developed in [12].…”

Section: Introductionmentioning

confidence: 99%

Robust Z-Estimators for Semiparametric Moment Condition Models

Toma

2023

Entropy

View full text Add to dashboard Cite

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.

show abstract