IDEAS home Printed from https://ideas.repec.org/p/ete/kbiper/616160.html
   My bibliography  Save this paper

Asymptotic post-selection inference for Akaike’s information criterion

Author

Listed:
  • Ali Charkhi
  • Gerda Claeskens

Abstract

Ignoring the model selection step in inference after selection is harmful. This paper studies the asymptotic distribution of estimators after model selection using the Akaike information criterion. First, we consider the classical setting in which a true model exists and is included in the candidate set of models. We exploit the overselection property of this criterion in the construction of a selection region, and obtain the asymptotic distribution of estimators and linear combinations thereof conditional on the selected model. The limiting distribution depends on the set of competitive models and on the smallest overparameterized model. Second, we relax the assumption about the existence of a true model, and obtain uniform asymptotic results. We use simulation to study the resulting postselection distributions and to calculate confidence regions for the model parameters. We apply the method to data.

Suggested Citation

  • Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for Akaike’s information criterion," Working Papers of Department of Decision Sciences and Information Management, Leuven 616160, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  • Handle: RePEc:ete:kbiper:616160
    as

    Download full text from publisher

    File URL: https://lirias.kuleuven.be/retrieve/499264
    File Function: Asymptotic post-selection inference for Akaike’s information criterion
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Akaike information criterion; Confidence region; Likelihood model; Model selection; post-selection inference;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ete:kbiper:616160. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://feb.kuleuven.be/KBI .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: library EBIB (email available below). General contact details of provider: https://feb.kuleuven.be/KBI .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.