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- Surveys and statistical programs – Documentation: 11-522-X19990015672Description:
Data fusion as discussed here means to create a set of data on not jointly observed variables from two different sources. Suppose for instance that observations are available for (X,Z) on a set of individuals and for (Y,Z) on a different set of individuals. Each of X, Y and Z may be a vector variable. The main purpose is to gain insight into the joint distribution of (X,Y) using Z as a so-called matching variable. At first however, it is attempted to recover as much information as possible on the joint distribution of (X,Y,Z) from the distinct sets of data. Such fusions can only be done at the cost of implementing some distributional properties for the fused data. These are conditional independencies given the matching variables. Fused data are typically discussed from the point of view of how appropriate this underlying assumption is. Here we give a different perspective. We formulate the problem as follows: how can distributions be estimated in situations when only observations from certain marginal distributions are available. It can be solved by applying the maximum entropy criterium. We show in particular that data created by fusing different sources can be interpreted as a special case of this situation. Thus, we derive the needed assumption of conditional independence as a consequence of the type of data available.
Release date: 2000-03-02 - Articles and reports: 12-001-X19980024354Description:
This article deals with an attempt to cross-tabulate two categorical variables, which were separately collected from two large independent samples, and jointly collected from one small sample. It was assumed that the large samples have a large set of common variables. The proposed estimation technique can be considered a mix between calibration techniques and statistical matching. Through calibration techniques, it is possible to incorporate the complex designs of the samples in the estimation procedure, to fulfill some consistency requirements between estimates from various sources, and to obtain fairly unbiased estimates for the two-way table. Through the statistical matching techniques, it is possible to incorporate a relatively large set of common variables in the calibration estimation, by means of which the precision of the estimated two-way table can be improved. The estimation technique enables us to gain insight into the bias generally obtained, in estimating the two-way table, by sole use of the large samples. It is shown how the estimation technique can be useful to impute values of the one large sample (donor source) into the other large sample (host source). Although the technique is principally developed for catagorical variables Y and Z, with a minor modification, it is also applicable for continuous variables Y and Z.
Release date: 1999-01-14
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- Articles and reports: 12-001-X19980024354Description:
This article deals with an attempt to cross-tabulate two categorical variables, which were separately collected from two large independent samples, and jointly collected from one small sample. It was assumed that the large samples have a large set of common variables. The proposed estimation technique can be considered a mix between calibration techniques and statistical matching. Through calibration techniques, it is possible to incorporate the complex designs of the samples in the estimation procedure, to fulfill some consistency requirements between estimates from various sources, and to obtain fairly unbiased estimates for the two-way table. Through the statistical matching techniques, it is possible to incorporate a relatively large set of common variables in the calibration estimation, by means of which the precision of the estimated two-way table can be improved. The estimation technique enables us to gain insight into the bias generally obtained, in estimating the two-way table, by sole use of the large samples. It is shown how the estimation technique can be useful to impute values of the one large sample (donor source) into the other large sample (host source). Although the technique is principally developed for catagorical variables Y and Z, with a minor modification, it is also applicable for continuous variables Y and Z.
Release date: 1999-01-14
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- Surveys and statistical programs – Documentation: 11-522-X19990015672Description:
Data fusion as discussed here means to create a set of data on not jointly observed variables from two different sources. Suppose for instance that observations are available for (X,Z) on a set of individuals and for (Y,Z) on a different set of individuals. Each of X, Y and Z may be a vector variable. The main purpose is to gain insight into the joint distribution of (X,Y) using Z as a so-called matching variable. At first however, it is attempted to recover as much information as possible on the joint distribution of (X,Y,Z) from the distinct sets of data. Such fusions can only be done at the cost of implementing some distributional properties for the fused data. These are conditional independencies given the matching variables. Fused data are typically discussed from the point of view of how appropriate this underlying assumption is. Here we give a different perspective. We formulate the problem as follows: how can distributions be estimated in situations when only observations from certain marginal distributions are available. It can be solved by applying the maximum entropy criterium. We show in particular that data created by fusing different sources can be interpreted as a special case of this situation. Thus, we derive the needed assumption of conditional independence as a consequence of the type of data available.
Release date: 2000-03-02