Recent developments in PROGRESS. The least median of squares (LMS) regression method is highly robust to outliers in the data. It can be computed by means of PROGRESS (from Program for RObust reGRESSion). After ten years we have developed a new version of PROGRESS, which also computes the least trimmed squares (LTS) method. We will discuss the various new features of PROGRESS, with emphasis on the algorithmic aspects.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Bertsimas, Dimitris; Mazumder, Rahul: Least quantile regression via modern optimization (2014)
- Huang, Xiaolin; Shi, Lei; Pelckmans, Kristiaan; Suykens, Johan A. K.: Asymmetric (\nu)-tube support vector regression (2014)
- Nunkesser, Robin; Morell, Oliver: An evolutionary algorithm for robust regression (2010)
- Bernholt, Thorsten; Nunkesser, Robin; Schettlinger, Karen: Computing the least quartile difference estimator in the plane (2007)
- Mount, David M.; Netanyahu, Nathan S.; Romanik, Kathleen; Silverman, Ruth; Wu, Angela Y.: A practical approximation algorithm for the LMS line estimator (2007)
- Rousseeuw, Peter J.; Van Driessen, Katrien: Computing lts regression for large data sets (2006) ioport
- Hennig, Christian: Clusters, outliers, and regression: Fixed point clusters (2003)
- Sanford Weisberg: Dimension Reduction Regression in R (2002) not zbMATH
- Rousseeuw, Peter J.; Hubert, Mia: Recent developments in PROGRESS (1997)