Download or read book Robust Estimators in Generalized Linear Models written by Tomáš Hobza and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Inference for Generalized Linear Models written by Sahar Hosseinian and published by . This book was released on 2009 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
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.
Download or read book Robust Estimation in Familial and Longitudinal Models written by Wasimul Bari and published by . This book was released on 2007 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modern Methods for Robust Regression written by Robert Andersen and published by SAGE Publications. This book was released on 2007-09-06 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Methods for Robust Regression offers a brief but in-depth treatment of various methods for detecting and properly handling influential cases in regression analysis. This volume, geared toward both future and practicing social scientists, is unique in that it takes an applied approach and offers readers empirical examples to illustrate key concepts. It is ideal for readers who are interested in the issues related to outliers and influential cases. Key Features Defines key terms necessary to understanding the robustness of an estimator: Because they form the basis of robust regression techniques, the book also deals with various measures of location and scale. Addresses the robustness of validity and efficiency: After having described the robustness of validity for an estimator, the author discusses its efficiency. Focuses on the impact of outliers: The book compares the robustness of a wide variety of estimators that attempt to limit the influence of unusual observations. Gives an overview of some traditional techniques: Both formal statistical tests and graphical methods detect influential cases in the general linear model. Offers a Web appendix: This volume provides readers with the data and the R code for the examples used in the book. Intended Audience This is an excellent text for intermediate and advanced Quantitative Methods and Statistics courses offered at the graduate level across the social sciences.
Download or read book Robust Estimation in Linear Model With Many Instruments written by Mikkel Soelvsten and published by . This book was released on 2005 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first chapter of this dissertation considers a new class of robust estimators in a linear instrumental variables (IV) model with many instruments. The estimators are generalized method of moments (GMM) estimators, and the class includes the limited maximum likelihood estimator (LIML) as a special case. Each estimator in the class is consistent and asymptotically normal under many instruments asymptotics, and this chapter provides consistent variance estimators that are of the ``sandwich'' type and can be used to conduct asymptotically correct inference. Furthermore, this chapter characterizes an optimal robust estimator among the members of the class. Compared to LIML, the optimal robust estimator is less influenced by outliers and more efficient under thick-tailed error distributions. In an empirical example (Angrist and Krueger, 1991), the optimal robust estimator is approximately 80 percent more efficient than LIML. The second chapter of this dissertation provides a central limit theorem based on Stein's method (Stein, 1972) which is an integral component in the proof of the main theorem in the first chapter. It also appears to be general enough in its scope that it can be applied to a variety of other problems.
Download or read book Robust Estimation and Inference for Generalized Linear Models written by Eva Cantoni and published by . This book was released on 1999 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Estimation and Inference for Generalized Linear Models written by Eva Cantoni and published by . This book was released on 1999 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Mixed Model Analysis written by Jiang Jiming and published by World Scientific. This book was released on 2019-04-10 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models.This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications.
Download or read book Robust Regression Estimation in Generalized Linear Models written by Nor Aishah Hamzah and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Statistics written by Ricardo A. Maronna and published by John Wiley & Sons. This book was released on 2019-01-04 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.
Download or read book Robust Regression written by Kenneth D. Lawrence and published by Routledge. This book was released on 2019-05-20 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Robust Penalized M estimators for Generalized Linear and Additive Models written by Marco Andrés Avella Medina and published by . This book was released on 2016 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Conditionally Unbiased Bounded Influence Robust Regression with Applications to Generalized Linear Models written by H. R. Kunsch and published by . This book was released on 1987 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document proposes a class of bounded influence robust regression estimators with conditionally unbiased estimating functions given the design. Optimal estimators are found within this class. Applications are made to generalized linear models. An example applying logistic regression to food stamp data is discussed. Keywords: Asymptotic bias; Generalized linear models; Linear regression.
Download or read book Extending the Linear Model with R written by Julian J. Faraway and published by CRC Press. This book was released on 2016-03-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.
Download or read book Partially Linear Models written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.
