Localized Model Selection for Regression
نویسنده
چکیده
Research on model/procedure selection has focused on selecting a single model globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be improved when selection of a model is allowed to depend on location. We consider localized model selection and derive their theoretical properties.
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تاریخ انتشار 2007