Government policy questions and media planning tasks may be answered by this data set. It covers a wide range of different aspects of statistical matching that in Europe typically is called data fusion. A book about statistical matching will be of interest to researchers and practitioners, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and official statistics will find it useful.
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"Statistical matching is one of the methods for the imputation of missing results from survey sampling...This book is totally focused on methods, applications, and results. It devotes little space to the presentation and derivation of various theorems." Technometrics, May 2004
Introduction * Principles of the Statistical Matching Process * Traditional Appraoches to Data Function * Alternative Approaches to Merge Data Sets * Empirical Evaluation of Alternative Approaches * Synopsis and Outlook
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Government policy questions and media planning tasks may be answered by this data set. It covers a wide range of different aspects of statistical matching that in Europe typically is called data fusion. A book about statistical matching will be of intere. Codice articolo 5912577
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 264 pp. Englisch. Codice articolo 9780387955162
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Taschenbuch. Condizione: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. Codice articolo 9780387955162
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9780387955162
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