## The Annals of Statistics

### Can one estimate the conditional distribution of post-model-selection estimators?

#### Abstract

We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined procedure resulting from first selecting a model (e.g., by a model selection criterion such as AIC or by a hypothesis testing procedure) and then estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data set. We show that it is impossible to estimate this distribution with reasonable accuracy even asymptotically. In particular, we show that no estimator for this distribution can be uniformly consistent (not even locally). This follows as a corollary to (local) minimax lower bounds on the performance of estimators for this distribution. Similar impossibility results are also obtained for the conditional distribution of linear functions (e.g., predictors) of the post-model-selection estimator.

#### Article information

Source
Ann. Statist., Volume 34, Number 5 (2006), 2554-2591.

Dates
First available in Project Euclid: 23 January 2007

https://projecteuclid.org/euclid.aos/1169571807

Digital Object Identifier
doi:10.1214/009053606000000821

Mathematical Reviews number (MathSciNet)
MR2291510

Zentralblatt MATH identifier
1106.62029

#### Citation

Leeb, Hannes; Pötscher, Benedikt M. Can one estimate the conditional distribution of post-model-selection estimators?. Ann. Statist. 34 (2006), no. 5, 2554--2591. doi:10.1214/009053606000000821. https://projecteuclid.org/euclid.aos/1169571807

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