Stammdaten

Titel: A novel Bayesian approach for variable selection in linear regression models
Untertitel:
Kurzfassung:

A novel Bayesian approach to the problem of variable selection in multiple linear regression models is proposed. In particular, a hierarchical setting which allows for direct specification of a priori beliefs about the number of nonzero regression coefficients as well as a specification of beliefs that given coefficients are nonzero is presented. This is done by introducing a new prior for a random set which holds the indices of the predictors with nonzero regression coefficients. To guarantee numerical stability, a g-prior with an additional ridge parameter is adopted for the unknown regression coefficients. In order to simulate from the joint posterior distribution an intelligent random walk Metropolis–Hastings algorithm which is able to switch between different models is proposed. For the model transitions a novel proposal, which prefers to add a priori or empirically important predictors to the model and further tries to remove less important ones, is used. Testing the algorithm on real and simulated data illustrates that it performs at least on par and often even better than other well-established methods.

Finally, it is proven that under some nominal assumptions, the presented approach is consistent in terms of model selection.

Schlagworte: Variable selection Hierarchical Bayes g-prior with ridge parameter Model uncertainty Metropolis–Hastings algorithm Consistency
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Computational Statistics and Data Analysis
Computational Statistics and Data Analysis
zur Publikation
 ( Elsevier Ltd.; )
Erscheinungsdatum: 05.11.2019
Titel der Serie: -
Bandnummer: -
Heftnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 1 - 24

Identifikatoren

ISBN: -
ISSN: -
DOI: http://dx.doi.org/10.1016/j.csda.2019.106881
AC-Nummer: -
Homepage: https://www.elsevier.com/locate/csda
Open Access
  • Online verfügbar (Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Statistik
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Österreich
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 101018 - Statistik
Forschungscluster
  • Nachhaltigkeit
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Computational Statistics

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