Download or read book Robust and Accurate Inference for Generalized Linear Models written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the framework of generalized linear models, the nonrobustness of classical estimators and tests for the parameters is a well known problem and alternative methods have been proposed in the literature. These methods are robust and can cope with deviations from the assumed distribution. However, they are based on ̄rst order asymptotic theory and their accuracy in moderate to small samples is still an open question. In this paper we propose a test statistic which combines robustness and good accuracy for moderate to small sample sizes. We combine results from Cantoni and Ronchetti (2001) and Robinson, Ronchetti and Young (2003) to obtain a robust test statistic for hypothesis testing and variable selection which is asymptotically Â2¡distributed as the three classical tests but with a relative error of order O(n¡1). This leads to reliable inference in the presence of small deviations from the assumed model distribution and to accurate testing and variable selection even in moderate to small samples.

Download or read book Robust Second Order Accurate Inference for Generalized Linear Models written by Ndame Lô and published by . This book was released on 2006 with total page 29 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 Robust Inference for Generalized Linear Models written by Sahar Hosseinian and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust inference for generalized linear models with application to logistic regression written by Gianfranco Adimari and published by . This book was released on 2000 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accurate Finite Sample Inference for Generalized Linear Models and Models on Overidentifying Moment Conditions written by Ndame Lô and published by . This book was released on 2006 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classical inference in statistic and econometric models is typically carried out by means of asymptotic approximations to the sampling distribution of estimators and test statistics. These approximations often do not provide accurate p-values and confidences intervals, especially when the sample size is small. Moreover, even if the sample size is large, the accuracy can be poor due to model misspecification (nonrobustness). Several alternative techniques have been proposed in the statistic and econometric literature to improve the accuracy of clasical inference. In general, these alternatives address either the accuracy of the first-order approximations or the nonrobustness issue. However, the development of general procedures which are both robust and second order accurate is still an open question. In this thesis, we propose an alternative statistical test wich has both robustness and small sample properties for two large and important classes of models: Generalized Linear Models (GLM) and models on overidentifying moments conditions.

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 Applying Generalized Linear Models written by James K. Lindsey and published by Springer Science & Business Media. This book was released on 2008-01-15 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Download or read book Robust Inference Based on Quasi likelihoods for Generalized Linear Models and Longitudinal Data written by Eva Cantoni and published by . This book was released on 2001 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accurate Finite Sample Inference for Generalized Linear Models and Models on Overidentifying Moment Conditions written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2008-05-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

Download or read book Generalized Linear Models With Examples in R written by Peter K. Dunn and published by Springer. This book was released on 2018-11-10 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities. The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. Other features include: • Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals • Nearly 100 data sets in the companion R package GLMsData • Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session

Download or read book Generalized Linear Models written by Raymond H. Myers and published by John Wiley & Sons. This book was released on 2012-01-20 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2018-04-17 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC methods to fit GLMs Contains numerous examples from business, medicine, engineering, and the social sciences Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods Offers the data sets and solutions to the exercises online Describes the components of good statistical practice to improve scientific validity and reproducibility of results. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.

Download or read book Foundations of Linear and Generalized Linear Models written by Alan Agresti and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Download or read book Generalized Linear Models written by P. McCullagh and published by Routledge. This book was released on 2019-01-22 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot