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Einband grossHandbook of Measurement Error Models
ISBN/GTIN

Handbook of Measurement Error Models

E-BookPDF0 - No protectionE-Book
592 Seiten
Englisch
Taylor & Franciserschienen am17.10.20211. Auflage
Reference text for statistical methods and applications for measurement error models for: researchers who work with error-contaminated data, graduate students from statistics and biostatistics, analysts in multiple fields, including medical research, biosciences, nutritional studies, epidemiological studies and environmental studies.mehr
Verfügbare Formate
BuchGebunden
EUR283,50
TaschenbuchKartoniert, Paperback
EUR104,50
E-BookEPUB0 - No protectionE-Book
EUR104,99
E-BookPDF0 - No protectionE-Book
EUR104,99

Produkt

KlappentextReference text for statistical methods and applications for measurement error models for: researchers who work with error-contaminated data, graduate students from statistics and biostatistics, analysts in multiple fields, including medical research, biosciences, nutritional studies, epidemiological studies and environmental studies.
Details
Weitere ISBN/GTIN9781351588591
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis0 - No protection
Erscheinungsjahr2021
Erscheinungsdatum17.10.2021
Auflage1. Auflage
Seiten592 Seiten
SpracheEnglisch
Dateigrösse31025 Kbytes
Illustrationen33 schwarz-weiße Abbildungen, 33 schwarz-weiße Zeichnungen
Artikel-Nr.5745703
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1. Measurement Error models - A brief account of past developments and modern advancements. 2. The impact of unacknowledged measurement error. 3. Identifiability in measurement error. 4. Partial learning of misclassification parameters. 5. Using instrumental variables to estimate models with mismeasured regressors. 6. Likelihood Methods for Measurement Error and Misclassification. 7. Regression calibration for covariate measurement error. 8. Conditional and corrected score methods. 9. Semiparametric methods for measurement error and misclassification. 10. Deconvolution kernel density estimation. 11. Nonparametric deconvolution by Fourier transformation and other related approaches. 12. Deconvolution with unknown error distribution. 13. Nonparametric inference methods for Berkson errors. 14. Nonparametric Measurement Errors Models for Regression. 15. Covariate measurement error in survival data. 16. Mixed effects models with measurement errors in time-dependent covariates. 17. Estimation in mixed-effects models with measurement error. 18. Measurement error in dynamic models . 19. Spatial exposure measurement error in environmental epidemiology. 20. Measurement error as a missing data problem. 21. Measurement error in causal inference. 23. Bayesian adjustment for misclassification. 24. Bayesian approaches for handling covariate measurement errormehr

Autor

Grace Y. Yi is Professor of Statistics at the University of Western Ontario where she holds a Tier I Canada Research Chair in Data Science. She is a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She authored the monograph Statistical Analysis with Measurement Error or Misclassification (2017, Springer).

Aurore Delaigle is Professor at the School of Mathematics and Statistics at the University of Melbourne. She is a Fellow of the Australian Academy of Science, a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She is a past recipient of the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS) and of the Moran Medal from the Australian Academy of Science.

Paul Gustafson is Professor and Head of the Department of Statistics at the University of British Columbia. He is a Fellow of the American Statistical Association, the 2020 Gold Medalist of the Statistical Society of Canada, and the author of the monograph Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments (2004, Chapman and Hall, CRC Press).
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