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

Robust regression and outlier detection



Robust regression and outlier detection ebook




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


Table 4: Estimated Parameters for the Regression Model of Variance Correction Values. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download. Robust Correlation as a Distance Metric. Table 3: Percentages of Categories of Events Discovered Using Port Clustering and Two-Stage. Agglomerative Hierarchical Clustering. Table 2: Benchmark Results for Combinations of Subset Size and MCD Repetitions. 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. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. Modeling the Z-score Tuning Parameters for the Port Correlation Algorithm. About robust regression, robust estimators and statistical procedures, outlier detection, extreme value theory, data cleaning, outlier detection in high dimensional data, non parametric statistics. Tuesday, 9 April 2013 at 13:07. 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.