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A new criterion for variable selection

Author

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  • Philips, R.
  • Guttman, I.

Abstract

The variable/model selection problem is reexamined from a Bayesian perspective using data splitting to establish a joint prior for the relevent parameters. This allows for the required integrations that have to be performed to be over the same dimensional parameter space. It also produces a result which is independent of the scaling of both the independent as well as dependent variables. The posterior probability of each model [infinity] is calculated, where the subscript [alpha] is used to index the subsets of the predictor variables. This probability is shown to be asymptotically equal to 1, if [alpha] is the correct model. A new model selection criterion is also derived from this expression. Examples using simulated data and real data sets are provided.

Suggested Citation

  • Philips, R. & Guttman, I., 1998. "A new criterion for variable selection," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 11-19, May.
    • Handle: RePEc:eee:stapro:v:38:y:1998:i:1:p:11-19
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    Cited by:

    1. Łukasz Jarosław Kozar & Robert Matusiak & Marta Paduszyńska & Adam Sulich, 2022. "Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach," Energies, MDPI, vol. 15(18), pages 1-21, September.
    2. Guttman, Irwin & Redondas, María Dolores, 2003. "A bayesian approach for predicting with polynomial regresión of unknown degree," DES - Working Papers. Statistics and Econometrics. WS ws032104, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Gilles Celeux & Mohammed El Anbari & Jean-Michel Marin & Christian P. Robert, 2010. "Regularization in Regression : Comparing Bayesian and Frequentist Methods in a Poorly Informative Situation," Working Papers 2010-43, Center for Research in Economics and Statistics.
    4. Adam Sulich & Letycja Sołoducho-Pelc, 2022. "Changes in Energy Sector Strategies: A Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, September.

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