منابع مشابه
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملRobust High-Dimensional Linear Regression
The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of learning has made it a natural target for adversarial manipulation of trainin...
متن کاملRobust linear least squares regression
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متن کاملProperties of Preconditioners for Robust Linear Regression
In this paper, we consider solving the robust linear regression problem by an inexact Newton method and an iteratively reweighted least squares method. We show that each of these methods can be combined with the preconditioned conjugate gradient least square algorithm to solve large, sparse systems of linear equations efficiently. We consider the constant preconditioner and preconditioners base...
متن کاملRobust estimation of non-linear aspects
As a rst step for dealing with eecient robust estimation in non-linear models , we regard the problem of eecient robust estimation of non-linear aspects (functions) '() of the unknown parameter of a linear model. For robust estimation of a general non-linear aspect we propose estimators which are based on one-step-M-estimators and derive their asymptotic behaviour at the contaminated linear mod...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1993
ISSN: 0090-5364
DOI: 10.1214/aos/1176349401