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Robust regression

Kenneth D. Lawrence, Jeffrey L. Arthur · 2019

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About the book

Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation discusses generalized properties of Lp-estimators. Includes an algorithm for identifying outliers using least absolute value criterion in regression modeling reviews re-descending M-estimators studies Li linear regression proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model summarizes known properties of Li estimators for time series analysis examines ordinary least squares, latent root regression, and a robust regression weighting scheme and evaluates results from five different robust ridge regression estimators.

Book details

Year
2019
Language
English
Publisher
CRC Press LLC
Editions
4
ISBN
9781351418287

Data source: Wikidata

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