This book explores visualization and imputation techniques for
missing values and presents practical applications using the
statistical software R. It explains the concepts of common
imputation methods with a focus on visualization, description of
data problems and practical solutions using R, including modern
methods of robust imputation, imputation based on deep learning and
imputation for complex data. By describing the advantages,
disadvantages and pitfalls of each method, the book presents a
clear picture of which imputation methods are applicable given a
specific data set at hand. The material covered includes the
pre-analysis of data, visualization of missing values in incomplete
data, single and multiple imputation, deductive imputation and
outlier replacement, model-based methods including methods based on
robust estimates, non-linear methods such as tree-based and deep
learning methods, imputation of compositional data, imputation
quality evaluation from visual diagnostics to precision measures,
coverage rates and prediction performance and a description of
different model- and design-based simulation designs for the
evaluation. The book also features a topic-focused introduction to
R and R code is provided in each chapter to explain the practical
application of the described methodology. Addressed to researchers,
practitioners and students who work with incomplete data, the book
offers an introduction to the subject as well as a discussion of
recent developments in the field. It is suitable for beginners to
the topic and advanced readers alike.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
Statistics and Computing |
| Release date: |
September 2023 |
| First published: |
2023 |
| Authors: |
Matthias Templ
|
| Dimensions: |
235 x 155mm (L x W) |
| Pages: |
320 |
| Edition: |
1st ed. 2023 |
| ISBN-13: |
978-3-03-130072-1 |
| Categories: |
Books
Promotions
|
| LSN: |
3-03-130072-6 |
| Barcode: |
9783031300721 |
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