Robust regression

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  1. In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods.[1]
  2. Robust regression can be used in any situation in which you would use least squares regression.[2]
  3. The idea of robust regression is to weigh the observations differently based on how well behaved these observations are.[2]
  4. Stata’s rreg command implements a version of robust regression.[2]
  5. You can see the iteration history of both types of weights at the top of the robust regression output.[2]
  6. This paper develops the theory of Robust Regression (RR) and presents an effective convex approach that uses recent advances on rank minimization.[3]
  7. Robust regression methods provide an alternative to least squares regression by requiring less restrictive assumptions.[4]
  8. The rlm command in the MASS package command implements several versions of robust regression.[5]
  9. Robust regression is done by iterated re-weighted least squares (IRLS).[5]
  10. The command for running robust regression is rlm in the MASS package.[5]
  11. When comparing the results of a regular OLS regression and a robust regression, if the results are very different, you will most likely want to use the results from the robust regression.[5]
  12. It can be useful to run an experiment to directly compare the robust regression algorithms on the same dataset.[6]
  13. The main purpose of robust regression is to detect outliers and provide resistant (stable) results in the presence of outliers.[7]
  14. In order to achieve this stability, robust regression limits the influence of outliers.[7]
  15. And if you're using a robust regression estimator,use a percentile bootstrap.[8]
  16. Instead, use a robust regression estimatorand test hypotheses with a percentile bootstrapthat allows heteroscedasticity.[8]
  17. Second, always check the impact of removing leverage points,even when using a robust regression estimator.[8]
  18. The main use of robust regression in Prism is as a 'baseline' from which to remove outliers.[9]
  19. You may want to experiment with robust regression in order to better understand the outlier-removal method (which begins with robust regression).[9]
  20. In this study, we apply robust regression techniques to model stock returns and create stock selection models in a very large global stock universe.[10]
  21. In second section, we introduce the reader to modeling expected returns and make extensive use of robust regression techniques.[10]
  22. Robust regression models are used to estimate the determinants of total stock returns.[10]
  23. In robust regression one weights the data universally with its OLS residual; i.e., the larger the residual, the smaller the weight of the observation in the robust regression.[10]
  24. To be somewhat nitpicky, I would not quite say that outliers, heteroscedasticity, and non-normality don't matter with robust regression methods.[11]
  25. So you would use robust regression to protect against that possibility.[11]
  26. In this way a simulation step is added by using the Monte Carlo method to generate the best robust regression line.[12]
  27. The BLMS robust regression line was not affected by many types of outlying points in the data sets.[12]
  28. The robust regression toolbox was created by Tor Wager.[13]
  29. Increased sensitivity in neuroimaging analyses using robust regression.[13]
  30. Robust regression uses an iterative algorithm to identify observations with large residuals and down-weight them.[13]
  31. To use the CANlab Robust Regression toolbox, you'll need Matlab and three toolboxes on your Matlab path: SPM12, the CANlab Core Tools repository, and the CANlab Robust Regression toolbox.[13]
  32. In this paper, we analyze what happens in robust regression when .[14]
  33. Main Results We consider the following robust regression problem: let be Here, , , where , is a random (scalar) error independent of the vector .[14]
  34. As we will discuss later, our approach is not limited to this “standard” robust regression setting: we can, for instance, shed light on similar questions in weighted regression.[14]

소스

  1. ↑ Robust regression
  2. ↑ 2.0 2.1 2.2 2.3 Robust Regression | Stata Data Analysis Examples
  3. ↑ Robust Regression
  4. ↑ 13.3 - Robust Regression Methods
  5. ↑ 5.0 5.1 5.2 5.3 Robust Regression | R Data Analysis Examples
  6. ↑ Robust Regression for Machine Learning in Python
  7. ↑ 7.0 7.1 STAT Robust Regression Procedures
  8. ↑ 8.0 8.1 8.2 What is Robust Regression?
  9. ↑ 9.0 9.1 GraphPad Prism 9 Curve Fitting Guide
  10. ↑ 10.0 10.1 10.2 10.3 Investing in Global Markets: Big Data and Applications of Robust Regression
  11. ↑ 11.0 11.1 Robust regression - a better understanding
  12. ↑ 12.0 12.1 Robust linear regression taking into account errors in the predictor and response variables
  13. ↑ 13.0 13.1 13.2 13.3 canlab/RobustToolbox: Robust regression toolbox for neuroimaging data
  14. ↑ 14.0 14.1 14.2 On robust regression with high-dimensional predictors

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