Statistical Analysis with Measurement Error or Misclassification

Beschreibung

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. 
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. 

Brings together assorted methods concerning measurement error or misclassification in a single text, including updates of recent developments for a variety of settings Presents both statistical theory and applications in a coherent and systematic manner Highlights the essence of commonly used modeling and inference strategies Includes self-contained material of an individual topic in each chapter Provides exercises, discussion questions, and bibliographic notes at the end of each chapter to supplement the development in the text

Autor*in

Grace Y. Yi

Themen in »Statistical Analysis with Measurement Error or Misclassification«

measurement error misclassification mismeasurement survival analysis longitudinal data generalized linear models inference methods response variable

Stimmen zu »Statistical Analysis with Measurement Error or Misclassification«

“This book constitutes a comprehensive and thorough treatment of measurement error and misclassification in survival data, recurrent event data, longitudinal data, multi-state models, and case-control studies. … the book is well written and a pleasure to read.” (Rianne Jacobs, ISCB News, iscb.info, Issue 65, June, 2018)

“This book successfully collects, compiles, organizes, and presents the literature on the newly developed and earlier existing topics of measurement error models and misclassification in a crisp and concise way without losing the clarity in understanding. … I am sure it will stimulate researchers in and newcomers to this area.” (Shalabh, Mathematical Reviews, June, 2018)
“This book covers a wide range of topics in a unified framework where measurement error and misclassification problems receive careful treatments, from both practical and theoretical points of view. … This book can serve well as a textbook for a graduate-level course on measurement error in a (bio)statistics department … . Besides ample real life applications presented in the book, from which students can appreciate practical relevance of measurement error problems … .” (Xianzheng Huang, Journal of the American Statistical Association JASA, Vol. 113 (522), 2018)

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