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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.
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Robust Regression: Analysis and Applications
2019, CRC Press LLC
in English
1351418289 9781351418287
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Robust Regression: Analysis and Applications
2019, CRC Press LLC
in English
1351418262 9781351418263
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Robust Regression: Analysis and Applications
2019, CRC Press LLC
in English
1351418270 9781351418270
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Robust Regression: Analysis and Applications
2019, CRC Press LLC
in English
020374053X 9780203740538
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