Measurement Error In Longitudinal Data

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Measurement Error In Longitudinal Data

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Author : Alexandru Cernat
language : en
Publisher: Oxford University Press
Release Date : 2021

Measurement Error In Longitudinal Data written by Alexandru Cernat and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Mathematics categories.

Measurement Error in Longitudinal Data tackles the important issue of how to understand and estimate change in the context of imperfect data.

Measurement Error In Longitudinal Data

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Author : Alexandru Cernat
language : en
Publisher: Oxford University Press
Release Date : 2021-03-18

Measurement Error In Longitudinal Data written by Alexandru Cernat and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-18 with Science categories.

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.

Measurement Error In Longitudinal Data Without Validation Samples

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Author : Ruifeng Xu
language : en
Publisher:
Release Date : 2007

Measurement Error In Longitudinal Data Without Validation Samples written by Ruifeng Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Statistical Analysis With Measurement Error Or Misclassification

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Author : Grace Y. Yi
language : en
Publisher: Springer
Release Date : 2017-08-02

Statistical Analysis With Measurement Error Or Misclassification written by Grace Y. Yi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-02 with Mathematics categories.

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.

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

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Author : John P. Buonaccorsi
language : en
Publisher: CRC Press
Release Date : 2010-03-02

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 2010-03-02 with Mathematics categories.

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

Measurement Error In Linear Models For Longitudinal Data

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Author : Masahiro Takeuchi
language : en
Publisher:
Release Date : 1991

Measurement Error In Linear Models For Longitudinal Data written by Masahiro Takeuchi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Error analysis (Mathematics) categories.

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

Statistical Analysis Of Measurement Error Models And Applications

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Author : Philip J. Brown
language : en
Publisher: American Mathematical Soc.
Release Date : 1990

Statistical Analysis Of Measurement Error Models And Applications written by Philip J. Brown and has been published by American Mathematical Soc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Mathematics categories.

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.

The Work Of Raymond J Carroll

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Author : Marie Davidian
language : en
Publisher: Springer
Release Date : 2014-06-06

The Work Of Raymond J Carroll written by Marie Davidian and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-06 with Mathematics categories.

This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.