A Method For Estimation Of Generalized Linear Models When Explanatory Variables Contain Meaurement Error
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A Method For Estimation Of Generalized Linear Models When Explanatory Variables Contain Measurement Error
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Author : Roger Mark Sauter
language : en
Publisher:
Release Date : 1992
A Method For Estimation Of Generalized Linear Models When Explanatory Variables Contain Measurement Error written by Roger Mark Sauter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
A Method For Estimation Of Generalized Linear Models When Explanatory Variables Contain Meaurement Error
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Author : Roger Mark Sauter
language : en
Publisher:
Release Date : 1989
A Method For Estimation Of Generalized Linear Models When Explanatory Variables Contain Meaurement Error written by Roger Mark Sauter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Linear models (Statistics) categories.
This thesis considers the problem of estimating the linear parameters of generalized linear models (GLM), especially binomial and Poisson regression models, when the explanatory variable is subject to measurement error. In this situation, the dependence of the response variable on the observed explanatory variable cannot typically be modeled as a GLM; in particular, extra variability caused by measurement error cannot be accounted for using the binomial- or Poisson models. One strategy is to use existing methods adapted for extra-variability. The contribution of this thesis is to introduce an estimation method which makes use of Efron's (1986) double exponential family. The proposed method involves the calculation of maximum likelihood estimates from this density when it is used as an approximation to the true density of the response variables given the observed measurements. Efron's family of distributions offers an attractive alternative for approximating the distribution of the response variable given the observed explanatory variable and is closely related to the measurement error in GLM methods suggested by Armstrong (1985) and Prentice (1986). Properties of the proposed method are considered when the double exponential family model is thought to be correct and when it is thought to be an approximation. Special cases and examples are given to illustrate the estimation procedure and how to apply this method. Comparisons are made with other estimation procedures for the measurement error problem, both procedurally and numerically.
An Introduction To Generalized Linear Models
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Author : Annette J. Dobson
language : en
Publisher: CRC Press
Release Date : 2018-04-17
An Introduction To Generalized Linear Models written by Annette J. Dobson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-17 with Mathematics categories.
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.
An Introduction To Generalized Linear Models
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Author : Annette J. Dobson
language : en
Publisher: CRC Press
Release Date : 2008-05-12
An Introduction To Generalized Linear Models written by Annette J. Dobson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-12 with Mathematics categories.
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.
Foundations Of Linear And Generalized Linear Models
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Author : Alan Agresti
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-23
Foundations Of Linear And Generalized Linear Models written by Alan Agresti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-23 with Mathematics categories.
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.
Generalized Linear Models
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Author : P. McCullagh
language : en
Publisher: Routledge
Release Date : 2019-01-22
Generalized Linear Models written by P. McCullagh and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-22 with Mathematics categories.
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
Generalized Linear Models
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Author : John Patrick Hoffmann
language : en
Publisher: Addison-Wesley Longman
Release Date : 2004
Generalized Linear Models written by John Patrick Hoffmann and has been published by Addison-Wesley Longman this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Mathematics categories.
This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.
Partially Linear Models
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Author : Wolfgang Härdle
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Partially Linear Models written by Wolfgang Härdle and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
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.
Generalized Linear Models
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Author : Dipak K. Dey
language : en
Publisher: CRC Press
Release Date : 2000-05-25
Generalized Linear Models written by Dipak K. Dey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-05-25 with Mathematics categories.
This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers
Univariate And Multivariate General Linear Models
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Author : Kevin Kim
language : en
Publisher: CRC Press
Release Date : 2006-10-11
Univariate And Multivariate General Linear Models written by Kevin Kim and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-11 with Mathematics categories.
Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.