Title:      Statistical Inference After Model Selection

Authors:    Richard Berk, Lawrence  Brown, Linda Zhao

Entrydate:  2009-04-29 09:19:49

Keywords:   Statistical Inference, Model Selection

Abstract:   Conventional statistical inference requires that a
model of how the data were generated be known before the data
are analyzed. Yet in criminology, and in the social sciences
more broadly, a variety of model selection procedures are
routinely undertaken followed by statistical tests and
confidence intervals computed for a "final" model. In this
paper, we examine such practices and show how they are typically
misguided. The parameters being estimated are no longer well
defined, and post-model-selection sampling distributions are
mixtures with properties that are very different from what is
conventionally assumed. Confidence intervals and statistical
tests do not perform as they should. We examine in some detail
the specific mechanisms responsible. We also offer some
suggestions for better practice.

http://polmeth.wustl.edu/retrieve.php?id=902

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