Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments
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 Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision.

The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."
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Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments
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 Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision.

The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."
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Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

by Paul Gustafson
Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

by Paul Gustafson

Hardcover(UK ed.)

$210.00 
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Overview

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 Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision.

The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."

Product Details

ISBN-13: 9781584883357
Publisher: Taylor & Francis
Publication date: 09/25/2003
Series: Chapman & Hall/CRC Interdisciplinary Statistics , #13
Edition description: UK ed.
Pages: 200
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Paul Gustafson (University of British Columbia, Vancouver, Canada) (Author)

Table of Contents

Introduction. Impact of Mismeasured Continuous Variables. Impact of Mismeasured Categorical Variables. Adjustment for Mismeasured Continuous Variables. Adjustment for Mismeasured Categorical Variables. Further Topics. Appendix: Bayes-MCMC Inference. References.
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