David Preinerstorfer
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Prof. PhD
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Preinerstorfer
First name
David
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david.preinerstorfer@unisg.ch
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+41 71 224 27 67
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  • Publication
    Uniformly valid confidence intervals post-model-selection
    (Institute of Mathematical Statistics, 2020)
    Bachoc, François
    ;
    Preinerstorfer, David
    ;
    Steinberger, Lukas
    We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Berk et al. (Ann. Statist. 41 (2013) 802–837). In particular, the candidate models used can be misspecified, the target of inference is model-specific, and coverage is guaranteed for any data-driven model selection procedure. After developing a general theory, we apply our methods to practically important situations where the candidate set of models, from which a working model is selected, consists of fixed design homoskedastic or heteroskedastic linear models, or of binary regression models with general link functions. In an extensive simulation study, we find that the proposed confidence intervals perform remarkably well, even when compared to existing methods that are tailored only for specific model selection procedures.
    Type: journal article
    Journal: Annals of Statistics
    Volume: 48
    Issue: 1
    URL: https://doi.org/10.1214/19-AOS1815
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112550
  • Publication
    Non-monotonic penalizing for the number of structural breaks
    ( 2013)
    Reschenhofer, Erhard
    ;
    Preinerstorfer, David
    ;
    Steinberger, Lukas
    This paper first reduces the problem of detecting structural breaks in a random walk to that of finding the best subset of explanatory variables in a regression model and then tailors various subset selection criteria to this specific problem. Of particular interest are those new criteria, which are obtained by means of simulation using the efficient algorithm of Bai and Perron (J Appl Econom 18:1–22, 2003). Unlike conventional variable selection methods, which penalize new variables entering a model either in the same way (e.g., AIC and BIC) or milder (e.g., MRIC and FPEsub) than already included variables, they do not follow any monotonic penalizing scheme. In general, their non-monotonicity is more pronounced in the case of fat tails. The characteristics of the different criteria are illustrated using bootstrap samples from the Nile data set.
    Type: journal article
    Journal: Computational Statistics
    Volume: 28
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/90345