منابع مشابه
Robust weighted LAD regression
The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was least-sum of absolute deviations (LAD) regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the re...
متن کاملNonparametric LAD cointegrating regression
We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and dependent variable can be contemporaneously correlated. The asymptotic properties of the Nadaraya-Watson estimator are already examined in the literature. In this paper, we consider nonparametric least absolute deviation (LAD) regression and derive the asymptotic distributions of the local constant and ...
متن کاملRobust Locally Weighted Regression and Smoothing Scatterplots
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متن کاملRobust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso
The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce LAD-lasso. Compared with the LAD regression, LAD-lasso can do parameter estimation and variable selecti...
متن کاملWeighted Ridge MM-Estimator in Robust Ridge Regression with Multicollinearity
This study is about the development of a robust ridge regression estimator. It is based on weighted ridge MM-estimator (WRMM) and is believed to have potentials in remedying the problems of multicollinearity. The proposed method has been compared with several existing estimators, namely ordinary least squares (OLS), robust regression based on MM estimator, ridge regression (RIDGE), weighted rid...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2006
ISSN: 0167-9473
DOI: 10.1016/j.csda.2005.06.005