Information criteria for discriminating among alternative regression models / BEBR No. 455

Some decision rules for discriminating among alternative regression models are proposed and mutually compared. They are essentially based on the Akaike Information Criterion as well as the Kullback-Leibler Information Criterion (KLIC) : namely, the distance between a postulated model and the true unknown structure is measured by the KLIC. The proposed criteria combine the parsimony of parameters with the goodness of fit. Their relationships with conventional criteria are discussed in terms of a new concept of unbiasedness .

• Publication year

  1978

• Venue

  Unknown venue

• Publication date

  1978-11-01

• Fields of study

  Mathematics, Economics

• Identifiers
  DOI 10.2307/1913828
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

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