• Produktbild: Handbook of Measurement Error Models
  • Produktbild: Handbook of Measurement Error Models
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Handbook of Measurement Error Models

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.01.2024

Abbildungen

33 SW-Abb., 33 SW-Zeichn.

Herausgeber

Yi Grace Y. + weitere

Verlag

Taylor & Francis

Seitenzahl

592

Maße (L/B/H)

25,4/17,8/3,2 cm

Gewicht

1098 g

Sprache

Englisch

ISBN

978-1-03-207008-7

Beschreibung

Rezension

"This handbook provides detailed and comprehensive developments and methods for meta-analysis. Its insights and clear explanations make readers easily learn fundamental and advanced approaches to meta-analysis. This book is a valuable reference to develop new methods in meta-analysis and relevant materials provide motivating extensions in the future research."
- Biometrics

"Written by rigorous mathematical language, the papers in the book can be useful to professional statisticians and graduate students specializing in advanced regression modeling and analysis of data with measurement errors."
- Stan Lipovetsky in Technometrics, April 2023

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.01.2024

Abbildungen

33 SW-Abb., 33 SW-Zeichn.

Herausgeber

Verlag

Taylor & Francis

Seitenzahl

592

Maße (L/B/H)

25,4/17,8/3,2 cm

Gewicht

1098 g

Sprache

Englisch

ISBN

978-1-03-207008-7

EU-Ansprechpartner

Taylor & Francis Verlag GmbH
Kaufingerstraße 24
80331 München
DE
GPSR@taylorandfrancis.com

Herstelleradresse

Taylor & Francis Group
5 Howick Place
SW1P 1WG London
UK
GPSR@taylorandfrancis.com

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  • Produktbild: Handbook of Measurement Error Models
  • Produktbild: Handbook of Measurement Error Models
  • 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 error