Statistical Methods for Handling Incomplete Data

Statistical Methods for Handling Incomplete Data is a comprehensive book that covers recent theoretical findings and advances in statistical computing for analyzing incomplete data. It provides a rigorous treatment of imputation techniques, explores the propensity score method, and covers advanced topics such as data integration and neural network model imputation. The book is aimed at researchers and graduate students in statistics and can be used as a reference by applied researchers with a good quantitative background.

\n Format: Hardback
\n Length: 364 pages
\n Publication date: 19 November 2021
\n Publisher: Taylor & Francis Ltd
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Due to recent groundbreaking theoretical discoveries and advancements in statistical computing, the field of missing data analysis has witnessed a remarkable surge in techniques and applications. Statistical Methods for Handling Incomplete Data, a comprehensive textbook, provides a comprehensive and up-to-date exploration of the latest statistical theories and computational methods for analyzing incomplete data. This invaluable resource offers a thorough treatment of imputation techniques, encompassing multiple imputation, fractional imputation, and advanced methods such as propensity score method and estimation techniques for nonignorable missing data. Additionally, the book delves into the latest advancements of the propensity score method and estimation techniques for nonignorable missing data, presenting a survey sampling application and discussing advanced topics such as kernel ridge regression imputation and neural network model imputation.

The primary audience of Statistical Methods for Handling Incomplete Data consists of researchers and graduate students specializing in statistics, who will find it an indispensable reference. Moreover, applied researchers with a strong quantitative background can also benefit from this book as it offers practical insights and methodologies applicable to their work. To enhance the understanding of the subject matter, the book includes numerous real-world data examples and simulated examples, which help readers grasp the concepts and apply them in practical scenarios.

In conclusion, Statistical Methods for Handling Incomplete Data is a groundbreaking textbook that caters to the needs of researchers, graduate students, and applied researchers alike. By presenting the latest statistical theories, computational methods, and imputation techniques, this book empowers readers to handle incomplete data effectively and make informed decisions based on incomplete information. Its comprehensive coverage, real-world examples, and practical insights make it an essential resource for anyone interested in this field.

\n Weight: 862g\n
Dimension: 234 x 156 (mm)\n
ISBN-13: 9780367280543\n
Edition number: 2 New edition\n