Resource title

Asymptotic properties of model selection procedures in linear regression

Resource image

image for OpenScout resource :: Asymptotic properties of model selection procedures in linear regression

Resource description

In regression analysis there is typically a large collection of competing models available from which we want to select an appropriate one. This paper is concerned with asymptotic properties of procedures for selecting linear models, which are based on certain data-dependent criteria such as Mallows´ Cp, cross-validation and the generalized information criterion. We avoid the assumption of an adequate ("correct") model and allow the maximal model dimension to increase with the sample size. General asymptotic concepts are introduced, covering the usual ones of consistency and asymptotic optimality. The focus is on conditions for penalizing the model complexity which are necessary to optain the different optimalities. For example, the consistency of a procedure is decided by the interplay between these penalties, the complexity of the class of model candidates, and some quantity describing the ability to identify "wrong" (pseudo-inadequate) models. Many results known from the literature appear as special cases or are slightly modified.

Resource author

Bernd Droge

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22243

Resource license

Adapt according to the presented license agreement and reference the original author.