performance criteria for outlier identifiers. One of those Criteria, the maximum asymptotic bias, is carried over here to … identifiers with bounded maximum asymptotic bias. …

model. All three estimators have the same breakdown point, but their explosion bias curves are different. It is shown that … under a gross error model the explosion bias curve of the new estimator performs better than the bias curves of the other …

competing FDI theories. Since the structure of existing FDI data is well known to induce selection bias, we extend BMA theory to … HeckitBMA in order to address model uncertainty in the presence of selection bias. We show that more than half of the previously …

Abstract This paper is focused on detailed aspects of the loss function rho and its derivative psi for an optimal bias … robust regression method that minimizes the maximum asymptotic bias subject to a constraint on normal distribution efficiency … the following aspects the optimal bias robust regression estimator: (1) The analytic form for the rho function; (2) A …

bias with respect to a Tukey-Huber mixture model that includes a standard linear regression model with normally …

to hold. In this paper we propose a crude analytical approach to study the large sample bias of estimators when all … normalized weights leads to a smaller bias compared to a simple IPW estimator. To analyze the question of when the use of two … misspecified models are better than one we derive necessary and sufficient conditions for when the DR estimator has a smaller bias …

experiment is to external validity bias. In particular, we propose a measure of external robustness by estimating how much …

have smaller bias that is flatter as a function of first step smoothing leading to improved small sample properties. Series …