Ebook: Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches

Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book.

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Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book.

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Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book.
Content:
Front Matter....Pages N2-xviii
Introduction....Pages 1-14
Frequentist Theory of Statistical Matching....Pages 15-43
Practical Applications of Statistical Matching....Pages 44-70
Alternative Approaches to Statistical Matching....Pages 71-127
Empirical Evaluation of Alternative Approaches....Pages 128-199
Synopsis and Outlook....Pages 200-207
Back Matter....Pages 208-241

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Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book.
Content:
Front Matter....Pages N2-xviii
Introduction....Pages 1-14
Frequentist Theory of Statistical Matching....Pages 15-43
Practical Applications of Statistical Matching....Pages 44-70
Alternative Approaches to Statistical Matching....Pages 71-127
Empirical Evaluation of Alternative Approaches....Pages 128-199
Synopsis and Outlook....Pages 200-207
Back Matter....Pages 208-241
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