There is more statistical data produced in today's modernsociety than ever before. This data is analysed andcross-referenced for innumerable reasons. However, many data setshave no shared element and are harder to combine and thereforeobtain any meaningful inference from. Statistical matching allowsjust that; it is the art of combining information from differentsources (particularly sample surveys) that contain no common unit.In response to modern influxes of data, it is an area of rapidlygrowing interest and complexity. Statistical Matching: Theoryand Practice introduces the basics of statistical matching,before going on to offer a detailed, up-to-date overview of themethods used and an examination of their practical applications.* Presents a unified framework for both theoretical and practicalaspects of statistical matching.* Provides a detailed description covering all the steps neededto perform statistical matching.* Contains a critical overview of the available statisticalmatching methods.* Discusses all the major issues in detail, such as theConditional Independence Assumption and the assessment ofuncertainty.* Includes numerous examples and applications, enabling thereader to apply the methods in their own work.* Features an appendix detailing algorithms written in the Rlanguage.Statistical Matching: Theory and Practice presents acomprehensive exploration of an increasingly important area. Idealfor researchers in national statistics institutes and appliedstatisticians, it will also prove to be an invaluable text forscientists and researchers from all disciplines engaged in themultivariate analysis of data collected from different sources.
