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Robust Estimation For Multiple Linear Regression With Cd Copy
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Book Synopsis Robust Estimation for Multiple Linear Regression [with CD Copy]. by : Shashi Shekhar
Download or read book Robust Estimation for Multiple Linear Regression [with CD Copy]. written by Shashi Shekhar and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation with Discrete Explanatory Variables by : Pavel Cizek
Download or read book Robust Estimation with Discrete Explanatory Variables written by Pavel Cizek and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot be easily applied to models containing binary and categorical explanatory variables. Therefore, I design a robust estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains. Additionally, I propose an adaptive procedure that maximizes the efficiency of the proposed estimator for a given data set while preserving its robustness.
Book Synopsis Modern Methods for Robust Regression by : Robert Andersen
Download or read book Modern Methods for Robust Regression written by Robert Andersen and published by SAGE. This book was released on 2008 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering an in-depth treatment of robust and resistant regression, this volume takes an applied approach and offers readers empirical examples to illustrate key concepts.
Book Synopsis Robust Statistics, Data Analysis, and Computer Intensive Methods by : Helmut Rieder
Download or read book Robust Statistics, Data Analysis, and Computer Intensive Methods written by Helmut Rieder and published by Springer. This book was released on 1996 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a wide range of contributions on modern techniques which are becoming widely used in statistics. These methods include the bootstrap, nonparametric density estimation, robust regression, and projections and sections.
Book Synopsis Robust Estimation Based on Grouped-adjusted Data in Linear Regression Models by : Stanford University. Econometric Workshop
Download or read book Robust Estimation Based on Grouped-adjusted Data in Linear Regression Models written by Stanford University. Econometric Workshop and published by . This book was released on 1985 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation Technique and Robust Autocorrelation Diagnostic for Multiple Linear Regression Model with Autocorrelated Errors by : Hock Ann Lim
Download or read book Robust Estimation Technique and Robust Autocorrelation Diagnostic for Multiple Linear Regression Model with Autocorrelated Errors written by Hock Ann Lim and published by . This book was released on 2014 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Regression written by Lawrence and published by CRC Press. This book was released on 1989-12-11 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining theory, methodology, and applications in a unified survey, this important reference/text presents the most recent results in robust regression analysis, including properties of robust regression techniques, computational issues, forecasting, and robust ridge regression. It provides useful case studies so that students and engineers can apply these techniques to forecasting, quantitative business analysis, econometrics, marketing, statistics, and demand modeling. Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation ... discusses generalized properties of L[subscript p]-estimators ... includes an algorithm for identifying outliers using least absolute value criterion in regression modeling ... reviews redescending M-estimators ... studies L[subscript 1] linear regression ... proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model ... summarizes known properties of L[subscript 1] 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. Containing 120 tables and diagrams plus numerous bibliographic citations, Robust Regression: Analysis and Applications is the leading reference for applied statisticians, operations researchers, econometricians, marketing forecasters, business administration and management scientists, and industrial engineers as well as a text for graduate statistics or economics courses. Book jacket.
Book Synopsis Robust Estimation of Multiple Regression Model with Non-normal Error by : Wing-Keung Wong
Download or read book Robust Estimation of Multiple Regression Model with Non-normal Error written by Wing-Keung Wong and published by . This book was released on 2005 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation in Structured Linear Regression by : Lamine Mili
Download or read book Robust Estimation in Structured Linear Regression written by Lamine Mili and published by . This book was released on 1993 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Linear Regression Analysis by : Douglas C. Montgomery
Download or read book Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by Wiley-Interscience. This book was released on 2006-07-21 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and up-to-date introduction to the fundamentals of regression analysis The Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions. Illustrating all of the major procedures employed by the contemporary software packages MINITAB(r), SAS(r), and S-PLUS(r), the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss: * Indicator variables and the connection between regression and analysis-of-variance models * Variable selection and model-building techniques and strategies * The multicollinearity problem--its sources, effects, diagnostics, and remedial measures * Robust regression techniques such as M-estimators, and properties of robust estimators * The basics of nonlinear regression * Generalized linear models * Using SAS(r) for regression problems This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint(r) slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting. With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.
Book Synopsis On Efficient and Robust Estimation in Semiparametric Linear Regression Models with Missing Data by : Alex Catane Bajamonde
Download or read book On Efficient and Robust Estimation in Semiparametric Linear Regression Models with Missing Data written by Alex Catane Bajamonde and published by . This book was released on 1991 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Combination of Biased and Robust Estimation Techniques in Multiple Regression Models by : Ronald Gene Askin
Download or read book The Combination of Biased and Robust Estimation Techniques in Multiple Regression Models written by Ronald Gene Askin and published by . This book was released on 1979 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation Methods and Robust Multicollinearity Diagnostics for Multiple Regression Model in the Presence of High Leverage Collinearity-influential Observations by : Arezoo Bagheri
Download or read book Robust Estimation Methods and Robust Multicollinearity Diagnostics for Multiple Regression Model in the Presence of High Leverage Collinearity-influential Observations written by Arezoo Bagheri and published by . This book was released on 2011 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation and Regression by : Hai-Li Hsiang Wang
Download or read book Robust Estimation and Regression written by Hai-Li Hsiang Wang and published by . This book was released on 1981 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modern Regression Methods by : Thomas P. Ryan
Download or read book Modern Regression Methods written by Thomas P. Ryan and published by John Wiley & Sons. This book was released on 2008-11-10 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one." —The American Statistician "The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews) Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics. New features of the Second Edition include: A revised chapter on logistic regression, including improved methods of parameter estimation A new chapter focusing on additional topics of study in regression, including quantile regression, semiparametric regression, and Poisson regression A wealth of new and updated exercises with worked solutions An extensive FTP site complete with Minitab macros, which allow the reader to compute analyses, and specialized procedures Updated references at the end of each chapter that direct the reader to the appropriate resources for further study An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels. It is also a valuable reference for practicing statisticians, engineers, and physical scientists.
Book Synopsis Applied Linear Regression by : Sanford Weisberg
Download or read book Applied Linear Regression written by Sanford Weisberg and published by . This book was released on 1985-08-14 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simple linear regression; Multiple regression; Drawing conclusions; Weighted least squares, testing for lack of fit, general F-tests, and confidence ellipsoids; Diagnostics I, residuals and influence; Diagnostics II, symptoms and remedies; Model building I, defining new predictors; Model building I, collinearity and variable selection; Prediction; Incomplete data; Contents; Nonleast squares estimation; Generalizations of linear regression.
Book Synopsis Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models by : Pavel Čížek
Download or read book Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models written by Pavel Čížek and published by . This book was released on 2001 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: