The distribution of a linear predictor after model selection: Unconditional finite-sample distributions and asymptotic approximations
We analyze the (unconditional) distribution of a linear predictor that is constructed after a data-driven model selection step in a linear regression model. First, we derive the exact finite-sample cumulative distribution function (cdf) of the linear predictor, and a simple approximation to this (complicated) cdf. We then analyze the large-sample limit behavior of these cdfs, in the fixed-parameter case and under local alternatives.
|Date of creation:||Nov 2006|
|Date of revision:|
|Publication status:||Published in IMS Lecture Notes--Monograph Series 2006, Vol. 49, 291-311|
|Contact details of provider:|| Web page: http://arxiv.org/|
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- Leeb, Hannes & P tscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(01), pages 21-59, February.
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