Mon premier blog

Robust regression and outlier detection by Annick M. Leroy, Peter J. Rousseeuw

Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw ebook
Publisher: Wiley
ISBN: 0471852333, 9780471852339
Format: pdf
Page: 347

Econometrica 71 (6), 1695-1725. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. Aggregate Industry Productivity. An even more outlier robust linear regression technique is least median of squares, which is only concerned with the median error made on the training data, not each and every error. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. Leroy (1987), Robust Regression and Outlier. We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. High Performance Object Detection by Collaborative Learning of Joint Ranking of Granule Features. For data reconciliation, the SV regression Moreover, it is not so strict to tune the coefficients of the SV regression approach because of the robustness of the coefficients for the reconciled results. (2003), The Impact of Trade on Intra-Industry Reallocations and. WILEY–INTERSCIENCE PAPERBACK SERIES The Wiley–Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. Authors: Toward Coherent Object Detection And Scene Layout Understanding Robust RVM Regression Using Sparse Outlier Model. Another solution to mitigate these problems is to preprocess the data with an outlier detection algorithm that attempts either to remove outliers altogether or de-emphasize them by giving them less weight than other points when constructing the linear regression model. Furthermore, a support vector regression (SV regression) approach is proposed for simultaneous data reconciliation and gross error or outlier detection, which considers gross errors and outliers as model complexity so as to remove them.