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

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

Bjoerck A, Numerical methods for least squares problems. Econometrica 71 (6), 1695-1725. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. 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. 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. 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. (2003), The Impact of Trade on Intra-Industry Reallocations and. 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]. Aggregate Industry Productivity. While this rule is appropriate for symmetric, approximately Gaussian data distributions, highly asymmetric situations call for an outlier detection rule that treats upward-outliers and downward-outliers differently. Leroy (1987), Robust Regression and Outlier. 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. Like covMcd, and robust fitting procedures like lmrob and glmrob for linear models and generalized linear models (specifically, a robust logistic regression procedure for binomial data, and a robust Poisson regression procedure for count data), among others. Rousseeuw PJ, Leroy AM, Robust regression and outlier detection.