Measurement Error Models

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Measurement Error Models

Wayne A. Fuller

Author : Wayne A. Fuller
Publisher : John Wiley & Sons
Page : 474 pages
File Size : 50,9 Mb
Release : 2009-09-25
Category : Mathematics
ISBN : 9780470317334

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Measurement Error Models by Wayne A. Fuller Pdf

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.

Measurement Error

John P. Buonaccorsi

Author : John P. Buonaccorsi
Publisher : CRC Press
Page : 465 pages
File Size : 47,6 Mb
Release : 2010-03-02
Category : Mathematics
ISBN : 9781420066586

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Measurement Error by John P. Buonaccorsi Pdf

Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illu

Handbook of Measurement Error Models

Grace Y. Yi,Aurore Delaigle,Paul Gustafson

Author : Grace Y. Yi,Aurore Delaigle,Paul Gustafson
Publisher : CRC Press
Page : 648 pages
File Size : 53,6 Mb
Release : 2021-09-28
Category : Mathematics
ISBN : 9781351588591

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Handbook of Measurement Error Models by Grace Y. Yi,Aurore Delaigle,Paul Gustafson Pdf

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 in Nonlinear Models

Raymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. Crainiceanu

Author : Raymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. Crainiceanu
Publisher : CRC Press
Page : 484 pages
File Size : 43,8 Mb
Release : 2006-06-21
Category : Mathematics
ISBN : 9781420010138

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Measurement Error in Nonlinear Models by Raymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. Crainiceanu Pdf

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 and Misclassification in Statistics and Epidemiology

Paul Gustafson

Author : Paul Gustafson
Publisher : CRC Press
Page : 213 pages
File Size : 54,6 Mb
Release : 2003-09-25
Category : Mathematics
ISBN : 9780203502761

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Measurement Error and Misclassification in Statistics and Epidemiology by Paul Gustafson Pdf

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

Radiation Risk Estimation

Sergii Masiuk,Alexander Kukush,Sergiy Shklyar,Mykola Chepurny,Illya Likhtarov

Author : Sergii Masiuk,Alexander Kukush,Sergiy Shklyar,Mykola Chepurny,Illya Likhtarov
Publisher : Walter de Gruyter GmbH & Co KG
Page : 270 pages
File Size : 51,8 Mb
Release : 2017-03-06
Category : Mathematics
ISBN : 9783110433661

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Radiation Risk Estimation by Sergii Masiuk,Alexander Kukush,Sergiy Shklyar,Mykola Chepurny,Illya Likhtarov Pdf

This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies. Contents: Part I - Estimation in regression models with errors in covariates Measurement error models Linear models with classical error Polynomial regression with known variance of classical error Nonlinear and generalized linear models Part II Radiation risk estimation under uncertainty in exposure doses Overview of risk models realized in program package EPICURE Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident Elements of estimating equations theory Consistency of efficient methods Efficient SIMEX method as a combination of the SIMEX method and the corrected score method Application of regression calibration in the model with additive error in exposure doses

Measurement Errors in Surveys

Paul P. Biemer,Robert M. Groves,Lars E. Lyberg,Nancy A. Mathiowetz,Seymour Sudman

Author : Paul P. Biemer,Robert M. Groves,Lars E. Lyberg,Nancy A. Mathiowetz,Seymour Sudman
Publisher : John Wiley & Sons
Page : 802 pages
File Size : 53,8 Mb
Release : 2013-08-12
Category : Mathematics
ISBN : 0471692808

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Measurement Errors in Surveys by Paul P. Biemer,Robert M. Groves,Lars E. Lyberg,Nancy A. Mathiowetz,Seymour Sudman Pdf

WILEY-INTERSCIENCE PAPERBACK SERIES 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. "This book will be an aid to survey statisticians and to research workers who must work with survey data." –Short Book Reviews, International Statistical Institute Measurement Errors in Surveys documents the current state of the field, reports new research findings, and promotes interdisciplinary exchanges in modeling, assessing, and reducing measurement errors in surveys. Providing a fundamental approach to measurement errors, the book features sections on the questionnaire, respondents and responses, interviewers and other means of data collection, the respondent-interviewer relationship, and the effects of measurement errors on estimation and data analysis.

Measurement Error in Nonlinear Models

Raymond J. Carroll,David Ruppert,Leonard A. Stefanski

Author : Raymond J. Carroll,David Ruppert,Leonard A. Stefanski
Publisher : CRC Press
Page : 334 pages
File Size : 44,7 Mb
Release : 1995-07-06
Category : Mathematics
ISBN : 0412047217

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Measurement Error in Nonlinear Models by Raymond J. Carroll,David Ruppert,Leonard A. Stefanski Pdf

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.

Statistical Analysis of Measurement Error Models and Applications

Philip J. Brown,Wayne A. Fuller,American Mathematical Society

Author : Philip J. Brown,Wayne A. Fuller,American Mathematical Society
Publisher : American Mathematical Soc.
Page : 262 pages
File Size : 48,5 Mb
Release : 1990
Category : Mathematics
ISBN : 9780821851173

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Statistical Analysis of Measurement Error Models and Applications by Philip J. Brown,Wayne A. Fuller,American Mathematical Society Pdf

Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. This book treats general aspects of the measurement problem and features a discussion of the history of measurement error models.

Measurement Error

John P. Buonaccorsi

Author : John P. Buonaccorsi
Publisher : Unknown
Page : 437 pages
File Size : 40,6 Mb
Release : 2010
Category : Electronic
ISBN : OCLC:1132095542

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Measurement Error by John P. Buonaccorsi Pdf

"Accessible to a broad audience, this book explains how to model measurement error, the effects of ignoring it, and how to correct for it. More applied than most books on measurement error, it describes basic models and methods, their use in a range of application areas, and the associated terminology."--Résumé de l'éditeur

Measurement Error and Latent Variables in Econometrics

T. Wansbeek,E. Meijer

Author : T. Wansbeek,E. Meijer
Publisher : North Holland
Page : 464 pages
File Size : 46,5 Mb
Release : 2000-12-08
Category : Business & Economics
ISBN : STANFORD:36105110175218

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Measurement Error and Latent Variables in Econometrics by T. Wansbeek,E. Meijer Pdf

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.

Statistical Regression with Measurement Error

Chi-Lun Cheng,John W. Van Ness

Author : Chi-Lun Cheng,John W. Van Ness
Publisher : Wiley
Page : 0 pages
File Size : 54,9 Mb
Release : 2010-06-28
Category : Mathematics
ISBN : 047071106X

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Statistical Regression with Measurement Error by Chi-Lun Cheng,John W. Van Ness Pdf

Providing a general survey of the theory of measurement error models, including the functional, structural, and ultrastructural models, this book is written in the of the Kendall and Stuart Advanced Theory of Statistics set and, like that series, includes exercises at the end of the chapters. The goal is to emphasize the ideas and practical implications of the theory in a style that does not concentrate on the theorem-proof format.

Mixed Effects Models for Complex Data

Lang Wu

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 41,9 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Applying Quantitative Bias Analysis to Epidemiologic Data

Timothy L. Lash,Matthew P. Fox,Aliza K. Fink

Author : Timothy L. Lash,Matthew P. Fox,Aliza K. Fink
Publisher : Springer Science & Business Media
Page : 200 pages
File Size : 54,7 Mb
Release : 2011-04-14
Category : Medical
ISBN : 9780387879598

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Applying Quantitative Bias Analysis to Epidemiologic Data by Timothy L. Lash,Matthew P. Fox,Aliza K. Fink Pdf

Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.

Statistical Analysis with Measurement Error or Misclassification

Grace Y. Yi

Author : Grace Y. Yi
Publisher : Springer
Page : 479 pages
File Size : 46,9 Mb
Release : 2017-08-02
Category : Mathematics
ISBN : 9781493966400

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Statistical Analysis with Measurement Error or Misclassification by Grace Y. Yi Pdf

This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

Measurement Error Models

Measurement Error Models by Wayne A. Fuller Pdf

Measurement Error by John P. Buonaccorsi Pdf

Handbook of Measurement Error Models by Grace Y. Yi,Aurore Delaigle,Paul Gustafson Pdf

Measurement Error in Nonlinear Models by Raymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. Crainiceanu Pdf

Measurement Error and Misclassification in Statistics and Epidemiology by Paul Gustafson Pdf

Radiation Risk Estimation by Sergii Masiuk,Alexander Kukush,Sergiy Shklyar,Mykola Chepurny,Illya Likhtarov Pdf

Measurement Errors in Surveys by Paul P. Biemer,Robert M. Groves,Lars E. Lyberg,Nancy A. Mathiowetz,Seymour Sudman Pdf

Measurement Error in Nonlinear Models by Raymond J. Carroll,David Ruppert,Leonard A. Stefanski Pdf

Statistical Analysis of Measurement Error Models and Applications by Philip J. Brown,Wayne A. Fuller,American Mathematical Society Pdf

Measurement Error by John P. Buonaccorsi Pdf

Measurement Error and Latent Variables in Econometrics by T. Wansbeek,E. Meijer Pdf

Statistical Regression with Measurement Error by Chi-Lun Cheng,John W. Van Ness Pdf

Mixed Effects Models for Complex Data by Lang Wu Pdf

Applying Quantitative Bias Analysis to Epidemiologic Data by Timothy L. Lash,Matthew P. Fox,Aliza K. Fink Pdf

Statistical Analysis with Measurement Error or Misclassification by Grace Y. Yi Pdf

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