Robust Regression
Analysis and Applications
1st Edition
Published on 20. May 2019
310 pages
978-1-351-41828-7 (ISBN)
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 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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
ISBN-13
978-1-351-41828-7 (9781351418287)
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Schweitzer Classification
Other editions
Additional editions
Book
06/2020
1st Edition
Routledge
€73.90
Shipment within 15-20 days
Book
12/1989
1st Edition
CRC Press
€328.07
Article not available at the moment
Person
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.
Content
Part 1 Advances in Robust Regression, Part 2 Robust Regression Methods, Part 3 Forecasting and Robust Regression, Part 4 Robust Ridge Regression, Index.
