Tipo: materialTypeLabelLibro - General
Ubicación Física: 519.535 / B989 2018

Flexible imputation of missing data /

Autor: Buuren, Stef van.
Serie: Chapman & Hall/CRC Interdisciplinary Statistics.
Pié de imprenta: Boca Raton : CRC Press, 2018.
Edición: Second edition.
Descripción: 415 páginas ; ilustraciones ; 16 x 24 cm.
ISBN: 9781138588318.
Tema(s):
Contenido: I. Basics. 1. Introduction. 2. Multiple imputation. 3. Univariate missing data. 4. Multivariate missing data. 5. Analysis of imputed data. II. Advanced techniques. 6. Imputation in practice. 7. Multilevel multiple imputation. 8. Individiual causal effects. III. Case studies. 9. Measurement issues. 10. Selection issues. 11. Longitudinal data. IV. Extensions. 12. Conclusion.
Contenido: I. Fundamentos. 1. Introducción. 2. Imputación múltiple. 3. Datos faltantes univariados. 4. Datos faltantes multivariados. 5. Análisis de datos imputados. II. Técnicas avanzadas. 6. Imputación en la práctica. 7. Imputación múltiple multinivel. 8. Efectos causales individuales. III. Estudios de caso. 9. Problemas de medición. 10. Problemas de selección. 11. Datos longitudinales. IV. Extensiones. 12. Conclusión.
Resumen: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem.This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field.This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data. (Tomado de la guente).

Lista(s) en las que aparece este ítem: Adquisiciones Psicología 2017-
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Libro - Reserva Libro - Reserva Biblioteca Sede 4
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Colección General 519.535/B989/2018 (Navegar estantería) Ej. 1 Disponible 64475

I. Basics. 1. Introduction. 2. Multiple imputation. 3. Univariate missing data. 4. Multivariate missing data. 5. Analysis of imputed data. II. Advanced techniques. 6. Imputation in practice. 7. Multilevel multiple imputation. 8. Individiual causal effects. III. Case studies. 9. Measurement issues. 10. Selection issues. 11. Longitudinal data. IV. Extensions. 12. Conclusion.

I. Fundamentos. 1. Introducción. 2. Imputación múltiple. 3. Datos faltantes univariados. 4. Datos faltantes multivariados. 5. Análisis de datos imputados. II. Técnicas avanzadas. 6. Imputación en la práctica. 7. Imputación múltiple multinivel. 8. Efectos causales individuales. III. Estudios de caso. 9. Problemas de medición. 10. Problemas de selección. 11. Datos longitudinales. IV. Extensiones. 12. Conclusión.

Psicología

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem.

This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field.

This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data. (Tomado de la guente).

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