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.
General
Imprint: |
Taylor & Francis
|
Country of origin: |
United Kingdom |
Series: |
Chapman & Hall/CRC Interdisciplinary Statistics |
Release date: |
September 2021 |
First published: |
2018 |
Authors: |
Stef Van Buuren
|
Dimensions: |
234 x 156 x 30mm (L x W x T) |
Format: |
Paperback
|
Pages: |
444 |
Edition: |
2nd edition |
ISBN-13: |
978-1-03-217863-9 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-03-217863-9 |
Barcode: |
9781032178639 |
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