Journal of Official Statistics, Vol.16, No.4, 2000. pp. 295–319

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Borrowing Strength When Explicit Data Pooling Is Prohibited

When using regression models where units can be classified into distinct groups, similar „parameters in each group can be estimated via explicit data pooling, such as in hierarchical models. Sometimes, however, external constraints prohibit explicit data pooling. In this „article, I propose techniques that may be acceptable under such external constraints and yield more accurate estimates than those obtained by regressing separately in each group. These techniques utilize the information in multiple groups' parameter estimates to specify the model in each group, but ultimately estimate the parameters selected for each group's model using only that group's data. The techniques can be conceptualized as existing on a continuum ordered by how directly each relies on data pooling to make estimates; those techniques that look more like explicit data pooling are typically more accurate yet less likely to be acceptable. I present several methods for evaluating the procedure empirically.

Information pooling; legal constraints; hierarchical modeling; model selection.

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