Robust Regression and Outlier Detection eBook

Robust Regression and Outlier Detection

By: Peter J. Rousseeuw, Annick M. Leroy

Robust Regression and Outlier Detection

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Robust Regression and Outlier Detection Summary

Provides an applications-oriented introduction to robust regression and outlier detection, emphasising ?high-breakdown? methods which can cope with a sizeable fraction of contamination. Its self-contained treatment allows readers to skip the mathematical material which is concentrated in a few sections. Exposition focuses on the least median of squares technique, which is intuitive and easy to use, and many real-data examples are given. Chapter coverage includes robust multiple regression, the special case of one-dimensional location, algorithms, outlier diagnostics, and robustness in related fields, such as the estimation of multivariate location and covariance matrices, and time series analysis.

eBooks > Titles > Authors > Science & Technology > Mathematics > Peter J. Rousseeuw > Annick M. Leroy > Robust Regression and Outlier Detection