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- W207179610 abstract "In Chapter 1 we point out that the statistical matching task may be viewed as a problem of nonresponse; more precisely, the missing information is regarded as missing at random because the missingness is induced by the study design of the separate samples. The missing data are due to unasked questions and the missingness mechanism is regarded as ignorable which in principle makes the application of conventional multiple imputation techniques obvious. However, contrary to the traditional missingness patterns, statistical matching is characterized by its identification problem. The association of the variables never jointly observed is unidentifiable and cannot be estimated by means of likelihood inference. Therefore prior information has to be embedded in the estimation process. Statistically matched files tend to display conditional independence between the variables only observed in separate files. We show that the validity of the traditional matching techniques concerning the preservation of the true association of the variables never jointly observed depends on the explanatory power of the common variables. Following an approach published by Rubin (1987) we propose the use of multiple imputation techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI, sensitivity of the unconditional association of the (specific) variables not jointly observed can be displayed. In other words, different prior settings of conditional associations allow us to show the extent to which unconditional associations are determined by the common variables.KeywordsMultiple ImputationStatistical MatchConditional IndependenceImpute DataConditional Independence AssumptionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W207179610 created "2016-06-24" @default.
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- W207179610 date "2002-01-01" @default.
- W207179610 modified "2023-09-27" @default.
- W207179610 title "Synopsis and Outlook" @default.
- W207179610 doi "https://doi.org/10.1007/978-1-4613-0053-3_6" @default.
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