Models where the least trimmed squares and least median of squares estimators are maximum likelihood
The Least Trimmed Squares (LTS) and Least Median of Squares (LMS) estimators are popular robust regression estimators. The idea behind the estimators is to find, for a given h, a sub-sample of h good observations among n observations and estimate the regression on that sub-sample. We find models, ba...
Auteurs principaux: | Berenguer-Rico, V, Johansen, S, Nielsen, B |
---|---|
Format: | Journal article |
Langue: | English |
Publié: |
2019
|
Documents similaires
-
A model where the least trimmed squares estimator is maximum likelihood
par: Berenguer-Rico, V, et autres
Publié: (2023) -
Least trimmed squares: nuisance parameter free asymptotics
par: Berenguer Rico, V, et autres
Publié: (2025) -
A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling
par: Raudhah Zulkifli, et autres
Publié: (2022-01-01) -
Symmetrically trimmed least squares estimation for Tobit models
par: Powell, James
Publié: (2011) -
Large Sample Behavior of the Least Trimmed Squares Estimator
par: Yijun Zuo
Publié: (2024-11-01)