Robust Regression by Kenneth D Lawrence
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-
Probability and Statistical Inference, Second Edition
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Handbook of Beta Distribution and Its Applications
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Statistical Inference Based on Divergence Measures
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Bayesian Biostatistics
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Item Response Theory
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Response Surfaces: Designs and Analyses
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Survey Sampling
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Advanced Linear Models
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Growth Curves
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Randomized Response and Indirect Questioning Techniques in Surveys
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Lognormal Distributions
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A Primer in Probability
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Analytical Methods for Risk Management
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Introduction to Spatial Econometrics
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Statistical Methods in Discrimination Litigation
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Mathematical Statistics With Applications
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The Inverse Gaussian Distribution
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Asymptotics, Nonparametrics, and Time Series
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Probability and Statistical Inference
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Handbook of Empirical Economics and Finance
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Random Processes
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Seemingly Unrelated Regression Equations Models
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Parameter Estimation in Reliability and Life Span Models
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Applied Regression Analysis and Experimental Design
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Statistics of Quality
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Statistical Methods for Engineers and Scientists
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Econometrics
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Regression Analysis of Survival Data in Cancer Chemotherapy
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Handbook of Parallel Computing and Statistics
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Randomization Tests
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Multiple Comparisons, Selection and Applications in Biometry
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Introduction to Probability and Statistics
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Linear Least Squares Computations
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Sample Size Choice
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Bivariate Discrete Distributions
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The EM Algorithm and Related Statistical Models
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Statistics for the 21st Century
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Improving Efficiency by Shrinkage
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Bayesian Analysis of Linear Models
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.
| SKU | Unavailable |
| ISBN 13 | 9780367580186 |
| ISBN 10 | 0367580187 |
| Title | Robust Regression |
| Author | Kenneth D Lawrence |
| Series | Statistics: A Series Of Textbooks And Monographs |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | CRC Press |
| Year published | 2020-06-30 |
| Number of pages | 310 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |