The Matching Methodology: Some Statistical Properties

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9780387969701: The Matching Methodology: Some Statistical Properties

Incomplete-data problems arise naturally in many instances of statistical practice. One class of incomplete-data problems, which is relatively not well understood by statisticians, is that of merging micro-data files. Many Federal agencies use the methodology of file-merging to create comprehensive files from multiple but incomplete sources of data. The main objective of this endeavor is to perform statistical analyses on the synthetic data set generated by file- merging. In general, these analyses cannot be performed by analyzing the incomplete data sets separately. The validity and the efficacy of the file-merging methodology can be assessed by means of statistical models underlying the mechanisms which may generate the incomplete files. However, a completely satisfactory and unified theory of file-merging has not yet been developed This monograph is only a minor attempt to fill this void for unifying known models. Here, we review the optimal properties of some known matching strategies and derive new results thereof. However, a great number of unsolved problems still need the attention of very many researchers. One main problem still to be resolved is the development of appropriate inference methodology from merged files if one insists on using file merging methodology. If this monograph succeeds in attracting just a few more mathematical statisticians to work on this class of problems, then we will feel that our efforts have been successful.

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Contenuti:

1. Introduction and Summary.- 1.1 A Dichotomy of Matching Problems.- 1.2 A Paradigm.- 1.3 A General Set-up for Statistical Matching.- 1.4 The Statistical Matching Methodology.- 1.5 Constrained and Unconstrained Matching.- 1.6 Reliability of Synthetic Files.- 1.7 Summary.- 2. Merging Files of Data on Same Individuals.- 2.1 AGeneralModel.- 2.2 Notations.- 2.3 Model-based Matching Strategies.- 2.4 Repairing a Broken Random Sample.- 2.5 Reliability of Matching Strategies for Bivariate Data.- 2.6 An Optimality Property of the Matching Strategy ?*.- 2.7 Monotonicity of E(N(?*)) with respect to Dependence Parameters.- 2.8 Some Properties of Approximate Matching Strategies.- 2.9 Poisson Convergence of N(?*).- 2.10 Matching variables with independent errors model.- 3. Merging Files Of Data On Similar Individuals.- 3.1 Kadane’s Matching Strategies for Multivariate Normal Models.- 3.2 Alternatives to Statistical Matching Under Conditional Independence.- 3.3 An Empirical Evaluation of Certain Matching Strategies.- Appendix A.- Appendix B.- References.- Author Index.

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Prem K. Goel, Thirugnanasambandam Ramalingam
Editore: Springer-Verlag New York Inc., United States (1989)
ISBN 10: 0387969705 ISBN 13: 9780387969701
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Descrizione libro Springer-Verlag New York Inc., United States, 1989. Paperback. Condizione libro: New. 248 x 171 mm. Language: English . Brand New Book ***** Print on Demand *****.Incomplete-data problems arise naturally in many instances of statistical practice. One class of incomplete-data problems, which is relatively not well understood by statisticians, is that of merging micro-data files. Many Federal agencies use the methodology of file-merging to create comprehensive files from multiple but incomplete sources of data. The main objective of this endeavor is to perform statistical analyses on the synthetic data set generated by file- merging. In general, these analyses cannot be performed by analyzing the incomplete data sets separately. The validity and the efficacy of the file-merging methodology can be assessed by means of statistical models underlying the mechanisms which may generate the incomplete files. However, a completely satisfactory and unified theory of file-merging has not yet been developed This monograph is only a minor attempt to fill this void for unifying known models. Here, we review the optimal properties of some known matching strategies and derive new results thereof. However, a great number of unsolved problems still need the attention of very many researchers. One main problem still to be resolved is the development of appropriate inference methodology from merged files if one insists on using file merging methodology. If this monograph succeeds in attracting just a few more mathematical statisticians to work on this class of problems, then we will feel that our efforts have been successful. Softcover reprint of the original 1st ed. 1989. Codice libro della libreria AAV9780387969701

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Descrizione libro Springer, 1989. Paperback. Condizione libro: NEW. 9780387969701 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Codice libro della libreria HTANDREE0276241

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Prem K. Goel, Thirugnanasambandam Ramalingam
Editore: Springer-Verlag New York Inc., United States (1989)
ISBN 10: 0387969705 ISBN 13: 9780387969701
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Descrizione libro Springer-Verlag New York Inc., United States, 1989. Paperback. Condizione libro: New. 248 x 171 mm. Language: English . Brand New Book ***** Print on Demand *****. Incomplete-data problems arise naturally in many instances of statistical practice. One class of incomplete-data problems, which is relatively not well understood by statisticians, is that of merging micro-data files. Many Federal agencies use the methodology of file-merging to create comprehensive files from multiple but incomplete sources of data. The main objective of this endeavor is to perform statistical analyses on the synthetic data set generated by file- merging. In general, these analyses cannot be performed by analyzing the incomplete data sets separately. The validity and the efficacy of the file-merging methodology can be assessed by means of statistical models underlying the mechanisms which may generate the incomplete files. However, a completely satisfactory and unified theory of file-merging has not yet been developed This monograph is only a minor attempt to fill this void for unifying known models. Here, we review the optimal properties of some known matching strategies and derive new results thereof. However, a great number of unsolved problems still need the attention of very many researchers. One main problem still to be resolved is the development of appropriate inference methodology from merged files if one insists on using file merging methodology. If this monograph succeeds in attracting just a few more mathematical statisticians to work on this class of problems, then we will feel that our efforts have been successful. Softcover reprint of the original 1st ed. 1989. Codice libro della libreria AAV9780387969701

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Descrizione libro Springer, 1989. Paperback. Condizione libro: New. Softcover reprint of the origina. This item is printed on demand. Codice libro della libreria DADAX0387969705

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