Robust Regression
Analysis and Applications
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 fr…
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Produktdetails
- ISBN: 978-1-351-41828-7
- EAN: 9781351418287
- Produktnummer: 31848374
- Verlag: Taylor & Francis Ltd.
- Sprache: Englisch
- Erscheinungsjahr: 2019
- Seitenangabe: 310 S.
- Plattform: PDF
- Masse: 7'683 KB
Über den Autor
Kenneth D. Lawrence is a Professor of Management Science and Business Analytics at the Tuchman School of Management at the New Jersey Institute of Technology. Dr. Lawrence's professional employment includes over 20 years of technical management experience with AT&T as Director, Decision Support Systems and Marketing Demand Analysis, Hoffmann-La Roche, Inc., Prudential Insurance, and the U. S. Army in forecasting, marketing planning and research, statistical analysis and operations research. His professional experience is reflected in his research which has been cited in 267 journals, including: Computers and Operations Research, International Journal of Forecasting and the Journal of Marketing.
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