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Kenneth D.
Robust Regression
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3851320Robust Regressionhttps://www.gandhi.com.mx/robust-regression-9781351418270/phttps://gandhi.vtexassets.com/arquivos/ids/3941577/ffc98a12-4906-4e73-9f0f-a5b124362106.jpg?v=63838610915880000013121312MXNCRC PressInStock/Ebooks/<p>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 redescending 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.</p>...3787798Robust Regression13121312https://www.gandhi.com.mx/robust-regression-9781351418270/phttps://gandhi.vtexassets.com/arquivos/ids/3941577/ffc98a12-4906-4e73-9f0f-a5b124362106.jpg?v=638386109158800000InStockMXN99999DIEbook20199781351418270_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9781351418270_<p>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 redescending 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.</p>9781351418270_CRC Presslibro_electonico_dc164a56-d60d-33df-88a6-2138360460c5_9781351418270;9781351418270_9781351418270Kenneth D.InglésMéxicohttps://getbook.kobo.com/koboid-prod-public/taylorandfrancis-epub-2b9461c3-123e-4b91-9768-1f6a451da342.epub2019-05-20T00:00:00+00:00CRC Press