A01=Kenneth D. Lawrence
A01=Lawrence
advanced data analysis
Analyst Forecasts
Author_Kenneth D. Lawrence
Author_Lawrence
Bayesian Posterior
Category=PBT
Consensus Analyst Forecast
Data Sets
Dual Algorithm
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Expected Earnings Growth
Gompertz Model
IRLS Algorithm
Ith Lot
least absolute value estimation
Li estimators
Li linear regression
linear modeling techniques
Linear Programming Problem
Lp-estimators
M-estimators
outlier detection methods
Price Earnings Multiples
Quantile Quantile Plot
re-descending M-estimators
Regression Quantiles
Regressor Variable
Ridge Estimators
Ridge Regression
Ridge Regression Estimator
Robust Regression
robust regression modeling applications
robust regression weighting scheme
Robust Ridge
Specialized Solution Procedures
Standardized Residuals
statistical robustness theory
Time Series Model
Univariate Time Series
Univariate Time Series Forecast
Univariate Time Series Model
Product details
- ISBN 9780824781293
- Weight: 589g
- Dimensions: 210 x 280mm
- Publication Date: 11 Dec 1989
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weigh. Each observation discusses generalized properties of LP-estimators. It includes an algorithm for identifying outliers using least absolute value criterion, in regression modelling reviews re-descending M-estimators studies Li linear regres
KENNETH D. LAWRENCE is Adjunct Professor of Industrial and Systems Engineering at Rutgers University in Piscataway, New Jersey. His professional employment includes 20 years of experience in technical management positions in strategic planning and operations research with the U.S. Army Munitions Command, Prudential Insurance, Hoffmann-La Roche, AT&T Long Lines, and AT&T. The author or coauthor of several articles and book chapters on regression analysis and forecasting, he is an associate editor of the Journal of Statistical Computation and Simulation. His professional affiliations include the American Statistical Association, Operations Research Society of America, Institute of Industrial Engineers, Institute of Management Sciences, Institute of Decision Sciences, and Institute of Mathematical Statistics. Dr. Lawrence’s graduate education includes master’s degrees in statistics, operations research, industrial engineering, finance and management, as well as a doctoral degree in applied statistics and operations research from Rutgers University (1978). JEFFREY L. ARTHUR is Associate Professor of Statistics at Oregon State University in Corvallis, where he has taught since 1977. He is the author or coauthor of several articles on the computational issues of optimization problems in statistics. He is a member of the Institute of Management Sciences and Operations Research Society of America. Professor Arthur received the Ph.D. degree (1977) in operations research and industrial engineering from Purdue University.
