Robust Regression in Stata

Robust Regression in Stata

In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg. Unfortunately, these methods only resist to some specif...

Regression models for categorical dependent variables using Stata, 2nd Edition

Boosted Regression (Boosting): An introductory tutorial and a Stata plugin

Boosting, or boosted regression, is a recent data mining technique that has shown considerable success in predictive accuracy. This article gives an overview over boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie et al. (2001, p. 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regress...

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...

Robust Regression

An introduction to robustness in statistics, with emphasis on its relevance to regression analysis. The weaknesses of the least squares estimator are highlighted, and the idea of error in data re ned. Properties such as breakdown, e ciency and equivariance are discussed and, through consideration of M, S and MM-estimators in relation to these properties, the progressive nature of robust estimat...