Items related to Statistical Matching: Theory and Practice (Wiley Series...
Synopsis
There is more statistical data produced in today’s modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications.
- 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.
"synopsis" may belong to another edition of this title.
About the Author
Marcello D'Orazio is the author of Statistical Matching: Theory and Practice, published by Wiley.
From the Back Cover
There is more statistical data produced in today's modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications.
- 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.
From the Inside Flap
There is more statistical data produced in today's modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications.
- 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.
"About this title" may belong to another edition of this title.
- PublisherWiley
- Publication date2006
- ISBN 10 0470023538
- ISBN 13 9780470023532
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages268
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Hardcover. Condition: new. Hardcover. There is more statistical data produced in todays modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications. 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. There is more statistical data produced in today's modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780470023532
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Buch. Condition: Neu. Neuware - There is more statistical data produced in today's modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications.\* 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. Seller Inventory # 9780470023532
Quantity: 2 available