Measurement Error In Nonlinear Models

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Measurement Error In Nonlinear Models

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Author : Raymond J. Carroll
language : en
Publisher: CRC Press
Release Date : 1995-07-06

Measurement Error In Nonlinear Models written by Raymond J. Carroll and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-07-06 with Mathematics categories.

This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.

Measurement Error In Nonlinear Models

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Author : Raymond J. Carroll
language : en
Publisher: CRC Press
Release Date : 2006-06-21

Measurement Error In Nonlinear Models written by Raymond J. Carroll and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-21 with Mathematics categories.

It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

Measurement Error In Nonlinear Models

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Author : Raymond J. Carroll
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2006-06-21

Measurement Error In Nonlinear Models written by Raymond J. Carroll and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-21 with Mathematics categories.

It’s been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and extensively updated to offer the most comprehensive and up-to-date survey of measurement error models currently available. What’s new in the Second Edition? · Greatly expanded discussion and applications of Bayesian computation via Markov Chain Monte Carlo techniques · A new chapter on longitudinal data and mixed models · A thoroughly revised chapter on nonparametric regression and density estimation · A totally new chapter on semiparametric regression · Survival analysis expanded into its own separate chapter · Completely rewritten chapter on score functions · Many more examples and illustrative graphs · Unique data sets compiled and made available online In addition, the authors expanded the background material in Appendix A and integrated the technical material from chapter appendices into a new Appendix B for convenient navigation. Regardless of your field, if you’re looking for the most extensive discussion and review of measurement error models, then Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition is your ideal source.

Measurement Error In Nonlinear Models

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Author : Sandra Nolte
language : en
Publisher: LIT Verlag Münster
Release Date : 2010

Measurement Error In Nonlinear Models written by Sandra Nolte and has been published by LIT Verlag Münster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.

This book analyzes how the choice of a particular disclosure limitation method, namely additive and multiplicative measurement error, affects the quality of the data and limits its usefulness for empirical research. Generally, a disclosure limitation method can be regarded as a data filter that transforms the true data generating process. This book focuses explicitly on the consequences of additive and multiplicative measurement error for the properties of nonlinear econometric estimators. It investigates the extent to which appropriate econometric techniques can yield consistent and unbiased estimates of the true data generating process in the case of disclosure limitation. Sandra Nolte received her PhD in Economics at the University of Konstanz, Germany in 2008 and is a postdoctoral researcher at the Financial Econometric Research Centre at the Warwick Business School, UK since 2009. Her research areas include microeconometrics and financial econometrics.

Measurement Error Models

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Author : Wayne A. Fuller
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-25

Measurement Error Models written by Wayne A. Fuller 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 2009-09-25 with Mathematics categories.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries." -Biometrics "Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book." -Journal of the American Statistical Association "The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection." -Technometrics " . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work." -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.

Nonlinear Models For Repeated Measurement Data

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Author : Marie Davidian
language : en
Publisher: CRC Press
Release Date : 1995-06-01

Nonlinear Models For Repeated Measurement Data written by Marie Davidian and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-06-01 with Mathematics categories.

Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.

Measurement Error

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Author : John P. Buonaccorsi
language : en
Publisher: CRC Press
Release Date : 2023-01-09

Measurement Error written by John P. Buonaccorsi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-09 with Error analysis (Mathematics) categories.

This work describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems and misclassification in categorical models to regression models to more complex mixed and time series models. It covers correction methods based on

Measurement Error And Misclassification In Statistics And Epidemiology

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Author : Paul Gustafson
language : en
Publisher: CRC Press
Release Date : 2003-09-25

Measurement Error And Misclassification In Statistics And Epidemiology written by Paul Gustafson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-25 with Mathematics categories.

Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassi

Handbook Of Measurement Error Models

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Author : Grace Y. Yi
language : en
Publisher: CRC Press
Release Date : 2021-10-17

Handbook Of Measurement Error Models written by Grace Y. Yi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-17 with Mathematics categories.

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research

Measurement Error And Latent Variables In Econometrics

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Author : T. Wansbeek
language : en
Publisher: North Holland
Release Date : 2000-12-08

Measurement Error And Latent Variables In Econometrics written by T. Wansbeek and has been published by North Holland this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-12-08 with Business & Economics categories.

The book first discusses in depth various aspects of the well-known inconsistency that arises when explanatory variables in a linear regression model are measured with error. Despite this inconsistency, the region where the true regression coeffecients lies can sometimes be characterized in a useful way, especially when bounds are known on the measurement error variance but also when such information is absent. Wage discrimination with imperfect productivity measurement is discussed as an important special case. Next, it is shown that the inconsistency is not accidental but fundamental. Due to an identification problem, no consistent estimators may exist at all. Additional information is desirable. This information can be of various types. One type is exact prior knowledge about functions of the parameters. This leads to the CALS estimator. Another major type is in the form of instrumental variables. Many aspects of this are discussed, including heteroskedasticity, combination of data from different sources, construction of instruments from the available data, and the LIML estimator, which is especially relevant when the instruments are weak. The scope is then widened to an embedding of the regression equation with measurement error in a multiple equations setting, leading to the exploratory factor analysis (EFA) model. This marks the step from measurement error to latent variables. Estimation of the EFA model leads to an eigenvalue problem. A variety of models is reviewed that involve eignevalue problems as their common characteristic. EFA is extended to confirmatory factor analysis (CFA) by including restrictions on the parameters of the factor analysis model, and next by relating the factors to background variables. These models are all structural equation models (SEMs), a very general and important class of models, with the LISREL model as its best-known representation, encompassing almost all linear equation systems with latent variables. Estimation of SEMs can be viewed as an application of the generalized method of moments (GMM). GMM in general and for SEM in particular is discussed at great length, including the generality of GMM, optimal weighting, conditional moments, continuous updating, simulation estimation, the link with the method of maximum likelihood, and in particular testing and model evaluation for GMM. The discussion concludes with nonlinear models. The emphasis is on polynomial models and models that are nonlinear due to a filter on the dependent variables, like discrete choice models or models with ordered categorical variables.