- Presents a unified framework for both theoretical and practical aspects of statistical matching.
- Provides a detailed description covering all the steps needed to perform statistical matching.
- Contains a critical overview of the available statistical matching methods.
- Discusses all the major issues in detail, such as the Conditional Independence Assumption and the assessment of uncertainty.
- Includes numerous examples and applications, enabling the reader to apply the methods in their own work.
- Features an appendix detailing algorithms written in the R language.
Statistical Matching: Theory and Practice presents a comprehensive exploration of an increasingly important area. Ideal for researchers in national statistics institutes and applied statisticians, it will also prove to be an invaluable text for scientists and researchers from all disciplines engaged in the multivariate analysis of data collected from different sources.