Abstract
Statistical inference following a preliminary model selection step is common practice in most applied statistical analyses. In practice, statistical inference is frequently conducted in a classical manner thereby ignoring the model selection phase. Not surprisingly, this leads to invalid procedures. We review some recent attempts towards a coherent theory for statistical inference in the presence of model selection.
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