Robust regression
analysis and applications
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Contributions
- Lawrence, Kenneth D. - Contributor
- Arthur, Jeffrey L., 1952- - Contributor
Publication
1990 - M. Dekker, New York, New York (State)
Language
English
Word Count
71,750 words, Guess
Page Count
287 pages
Identifiers
- Open LibraryOL2202053M
- ISBN-100824781295
- OCLC Control Number1103441070
- OCLC Control Number20491946
- OCLC Control Numberrobustregression0000unse
and 3 more
- Library of Congress Control Number89023760
- Goodreads2765751
- LibraryThing4069322
Classifications
- DDC519.5/36
- LCCQA278.2 .R59 1990
Description
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.
Subjects
Series Statement
- Statistics, textbooks and monographs;
Other Editions
- Robust regression: analysis and applications
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