High breakdown-point regression estimators protect against large errors and data con- tamination. Motivated by some … general trimmed estimator under mild -mixing conditions and demon- strate its applicability in nonlinear regression, time …

High breakdown-point regression estimators protect against large errors and data contamination. We generalize the … concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood …)linear regression models. We derive here the consistency and asymptotic distribution of the proposed general trimmed estimator under …

High breakdown-point regression estimators protect against large errors and data contamination. We adapt and generalize … the concept of trimming used by many of these robust estimators so that it can be employed in the context of the … of overidentifying conditions, and demonstrate the application of GMTM in the instrumental variable regression. We also …

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically … semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but relatively imprecise …

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding … these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several …

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding … these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several …

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically …- metric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust and relatively imprecise …

The binary-choice regression models such as probit and logit are used to describe the effect of explanatory variables …-breakdown point) methods such as the maximum trimmed like- lihood are not applicable since, by trimming observations, they induce the …-choice regression, we con- sider a maximum symmetrically-trimmed likelihood estimator (MSTLE) and design a parameter-free adaptive …

The binary-choice regression models such as probit and logit are typically estimated by the maximum likelihood method … estimates by trimming observations.We propose a new robust estimator of binary-choice models based on a maximum symmetrically …

The least squares estimator is probably the most frequently used estimation method in regression analysis … estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot … estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains …