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210503 ||| eng |
| 020 |
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|a 9781119942283
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| 050 |
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4 |
|a R853.S7
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| 100 |
1 |
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|a Carpenter, James R.
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| 245 |
0 |
0 |
|a Multiple imputation and its application
|h Elektronische Ressource
|c James R. Carpenter and Michael G. Kenward, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, UK
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| 250 |
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|a First Edition 2013
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| 260 |
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|a Chichester, West Sussex, UK
|b Wiley
|c 2013
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| 300 |
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|a xviii, 345 Seiten
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| 505 |
0 |
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|a Introduction -- The multiple imputation procedure and its justification -- Multiple imputation of quantitative data -- Multiple imputation of binary and ordinal data -- Multiple imputation of unordered categorical data -- Nonlinear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust multiple imputation.
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| 653 |
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|a Datenanalyse
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| 653 |
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|a Fehlende Daten
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| 653 |
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|a Multiple imputation (Statistics)
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| 653 |
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|a Missing observations (Statistics)
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| 653 |
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|a Social sciences -- Statistical methods
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| 653 |
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|a Medical statistics
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| 653 |
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|a Medicine -- Research -- Statistical methods
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| 653 |
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|a Data Interpretation, Statistical
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| 653 |
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|a Biomedical Research -- Methods
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| 653 |
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|a Kenward, Michael G., 1956-
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| 700 |
1 |
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|a Kenward, Michael G.
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WILOB
|a Wiley Online Books
|
| 490 |
0 |
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|a Statistics in Practice
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| 028 |
5 |
0 |
|a 10.1002/9781119942283
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| 776 |
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|z 9780470740521
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| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781119942283
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 610.724
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| 520 |
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|a Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.
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