The Least Absolute Deviations Estimators in Generalized Regression Models
Author | : Kazumitsu Nawata |
Publisher | : |
Total Pages | : 36 |
Release | : 1990 |
Genre | : Estimation theory |
ISBN | : |
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Author | : Kazumitsu Nawata |
Publisher | : |
Total Pages | : 36 |
Release | : 1990 |
Genre | : Estimation theory |
ISBN | : |
Author | : P- Bloomfield |
Publisher | : Springer Science & Business Media |
Total Pages | : 363 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1468485741 |
Least squares is probably the best known method for fitting linear models and by far the most widely used. Surprisingly, the discrete L 1 analogue, least absolute deviations (LAD) seems to have been considered first. Possibly the LAD criterion was forced into the background because of the com putational difficulties associated with it. Recently there has been a resurgence of interest in LAD. It was spurred on by work that has resulted in efficient al gorithms for obtaining LAD fits. Another stimulus came from robust statistics. LAD estimates resist undue effects from a feyv, large errors. Therefore. in addition to being robust, they also make good starting points for other iterative, robust procedures. The LAD criterion has great utility. LAD fits are optimal for linear regressions where the errors are double exponential. However they also have excellent properties well outside this narrow context. In addition they are useful in other linear situations such as time series and multivariate data analysis. Finally, LAD fitting embodies a set of ideas that is important in linear optimization theory and numerical analysis. viii PREFACE In this monograph we will present a unified treatment of the role of LAD techniques in several domains. Some of the material has appeared in recent journal papers and some of it is new. This presentation is organized in the following way. There are three parts, one for Theory, one for Applicatior.s and one for Algorithms.
Author | : Michael R. Kosorok |
Publisher | : Springer Science & Business Media |
Total Pages | : 482 |
Release | : 2007-12-29 |
Genre | : Mathematics |
ISBN | : 0387749780 |
Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
Author | : Michael H. Kutner |
Publisher | : McGraw-Hill/Irwin |
Total Pages | : 1396 |
Release | : 2005 |
Genre | : Mathematics |
ISBN | : 9780072386882 |
Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Author | : David Birkes |
Publisher | : John Wiley & Sons |
Total Pages | : 248 |
Release | : 2011-09-20 |
Genre | : Mathematics |
ISBN | : 1118150244 |
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ".[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process." --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models --growth, compartmental, and multiphase --and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge "The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects." --Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
Author | : Takeshi Amemiya |
Publisher | : Harvard University Press |
Total Pages | : 540 |
Release | : 1985 |
Genre | : Business & Economics |
ISBN | : 9780674005600 |
The main features of this text are a thorough treatment of cross-section models—including qualitative response models, censored and truncated regression models, and Markov and duration models—and a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.
Author | : Takeshi Amemiya |
Publisher | : Cambridge University Press |
Total Pages | : 472 |
Release | : 2001-01-08 |
Genre | : Business & Economics |
ISBN | : 9780521662468 |
This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Author | : William A. Barnett |
Publisher | : Cambridge University Press |
Total Pages | : 512 |
Release | : 1991-06-28 |
Genre | : Business & Economics |
ISBN | : 9780521424318 |
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author | : Douglas C. Montgomery |
Publisher | : John Wiley & Sons |
Total Pages | : 704 |
Release | : 2021-02-03 |
Genre | : Mathematics |
ISBN | : 1119578744 |
INTRODUCTION TO LINEAR REGRESSION ANALYSIS A comprehensive and current introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book. The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data sets New material on generalized regression techniques The inclusion of JMP software in key areas Carefully condensing the text where possible Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
Author | : Michael Smithson |
Publisher | : CRC Press |
Total Pages | : 300 |
Release | : 2013-09-05 |
Genre | : Mathematics |
ISBN | : 1466551755 |
Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.The book provides br