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.

Format: Paperback / softback
Length: 380 pages
Publication date: 29 January 2024
Publisher: Taylor & Francis Ltd

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 employed in analyzing incomplete data. This invaluable resource offers a thorough treatment of imputation techniques, encompassing multiple imputation, fractional imputation, and more. It also delves into the latest advancements of the propensity score method and estimation techniques for nonignorable missing data. Furthermore, the book showcases a survey sampling application and includes a new chapter on Data Integration, further enhancing its relevance. Designed primarily for researchers and graduate students in the field of statistics, this book serves as a valuable reference for applied researchers with a strong quantitative background. It is enriched with numerous real-world data examples and simulated examples, aiding readers in grasping the methodologies and applying them to their research endeavors.

Statistical Methods for Handling Incomplete Data

In recent years, significant theoretical advancements and technological breakthroughs have propelled the field of missing data analysis forward. Researchers and practitioners have developed a wide array of techniques and applications to address the challenges posed by incomplete data. Statistical Methods for Handling Incomplete Data, a comprehensive textbook, serves as a valuable resource for those seeking to understand and apply these methods.

The book begins by introducing the fundamental concepts and theories of missing data analysis. It explores the mean score equation, a building block for developing the theory, and provides a comprehensive coverage of computational techniques for handling incomplete data. The authors discuss various imputation techniques, including multiple imputation, fractional imputation, and list-wise deletion, and critically evaluate their strengths and limitations.

Furthermore, the book delves into the latest advancements of the propensity score method and estimation techniques for nonignorable missing data. It discusses the use of kernel ridge regression imputation and neural network model imputation, which are powerful tools for handling complex missing data scenarios. The authors provide detailed explanations and examples to illustrate the practical applications of these methods.

In addition to imputation techniques, the book explores the application of missing data analysis in survey sampling. It discusses the design and analysis of survey samples with incomplete data, including the use of weighting and stratification techniques to address bias and improve the accuracy of estimates. The book also includes a chapter on Data Integration, which discusses the merging of multiple datasets with incomplete data.

The textbook is primarily aimed at researchers and graduate students in the field of statistics, providing them with a comprehensive and up-to-date understanding of the latest statistical theories and computational methods for handling incomplete data. It serves as a valuable reference for applied researchers with a strong quantitative background, as it includes numerous real-world data examples and simulated examples to help readers grasp the methodologies and apply them to their research endeavors.

In conclusion, Statistical Methods for Handling Incomplete Data is a comprehensive and essential textbook for anyone interested in analyzing incomplete data. With its extensive coverage of statistical theories, computational methods, and practical applications, the book provides a valuable resource for researchers, graduate students, and applied researchers alike. By leveraging the power of statistical analysis, we can unlock the full potential of incomplete data and make informed decisions based on reliable and accurate information.

Weight: 700g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032118130
Edition number: 2 ed