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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley

Robust regression and outlier detection. A different type of approach is to formulate the detection of differential splicing as an outlier detection problem, as in REAP (Regression-based Exon Array Protocol) or FIRMA (Finding Isoforms using Robust Multichip Analysis) [15,16]. The basis of the algorithm is Peter J. What is new is that MathWorks addded a wide set of support functions that simplify common analysis tasks like plotting, outlier detection, generating predictions, performing stepwise regression, applying robust regression. Here is a plot of post 96 observations . Authors: Toward Coherent Object Detection And Scene Layout Understanding Robust RVM Regression Using Sparse Outlier Model. Robust Nonlinear Optimization MATLAB Code Download Please have a look in RANSAC_4_Nonlinear.m This code is not heavily tested. The volcanic term is critical here – at the moment it looks to me as though the ENSO term is robust, and the solar fairly robust (and small) once a response function is included. Even if you exclude CCSM4 as an outlier the contribution to the trend since '96 from volcanic recovery in the model ensemble is about the same as the observed trend. High Performance Object Detection by Collaborative Learning of Joint Ranking of Granule Features. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. Robust Nonlinear Fitting by RANSAC (Outlier Tolerent Nonlinear Regression). The least squared regression with the lowest meadian squared error is chosen as the final model.