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Find a path to becoming a Missing Value Imputation. Learn more at:
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Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Missing Value Imputation.
Classic work on multiple imputation, one of the most popular missing data imputation techniques. It provides a detailed overview of the theory and practice of multiple imputation, and it includes numerous examples and exercises to help readers understand the concepts.
Classic work on multiple imputation, a widely used technique for handling missing data. It provides a comprehensive overview of the theory and practice of multiple imputation, making it an essential reference for researchers and practitioners alike.
This handbook provides a comprehensive overview of missing data imputation and estimation techniques, covering a wide range of topics from basic concepts to advanced methods. It valuable resource for researchers and practitioners who need to deal with missing data in their work.
Provides a broad overview of missing data analysis and imputation techniques, covering both theoretical and practical aspects. It is particularly useful for understanding the underlying principles and assumptions of different imputation methods.
Provides a comprehensive overview of multiple imputation, covering both theoretical and practical aspects. It valuable resource for researchers and practitioners who need to understand and apply multiple imputation techniques.
Provides a comprehensive overview of missing data imputation techniques, with a focus on practical applications. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Provides a comprehensive overview of missing data imputation techniques, with a focus on statistical analysis. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Focuses on missing data imputation and analysis in social science research, providing practical guidance on selecting and implementing appropriate methods. It valuable resource for researchers who need to deal with missing data in social science research.
Provides a practical guide to missing data imputation and analysis, focusing on the application of different methods in various research settings. It valuable resource for researchers and practitioners who need to deal with missing data in their research.
Provides a comprehensive overview of missing data imputation techniques with regularized regression. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Provides a comprehensive overview of missing data imputation techniques in longitudinal studies. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
Provides a non-technical overview of missing data imputation, making it accessible to a wide range of readers. It valuable resource for researchers and practitioners who need to deal with missing data but do not have a strong statistical background.
Provides a comprehensive overview of missing data imputation techniques in clinical studies. It is written in a clear and concise style, and it includes numerous examples and exercises to help readers understand the concepts.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/a5nxhi/missing
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