Statistical analysis with measurement error or misclassification. Strategy, method and application
type: Item
instance of: scholarly article
title: Statistical analysis with measurement error or misclassification. Strategy, method and application
zbMATH Open document ID: 1377.62012
DOI: 10.1007/978-1-4939-6640-0
author: Grace Y. Yi
published in: Springer Series in Statistics
publication date: 2017-04-03T00:00:00Z
review text: From the introduction: 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. The book 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.
Mathematics Subject Classification ID: 62-01
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62Jxx
Mathematics Subject Classification ID: 62Nxx
Mathematics Subject Classification ID: 62-02
zbMATH DE Number: 6699403
zbMATH Keywords: inference
zbMATH Keywords: measurement error
zbMATH Keywords: misclassification
zbMATH Keywords: survival data
zbMATH Keywords: recurrent event data
zbMATH Keywords: longitudinal data
zbMATH Keywords: covariate measurement error
zbMATH Keywords: multi-state models
zbMATH Keywords: error-prone data
zbMATH Keywords: case-control
zbMATH Keywords: mismeasured response
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-1-4939-6640-0
OpenAlex ID: W2741572302
description: scientific article; zbMATH DE number 6699403