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- W1544156720 abstract "This paper shows new ways to discriminate between regression models. The most appealing aspect is that the criteria come from all possible prediction exercises one can imagine, but may be computed directly from the residual sums of squares. The computational clue is the Binet-Cauchy theorem from linear algebra, whlch points the way to new possibilities, encompassing several recent proposals. The context is Bayesian with noninformative priors, but the basic results are also of interest from a Classical viewpoint. The use of predictions allows to derive Bayes Factors circumventing the problem that noninformative priors lead to probability statements of incomparable dimensions. These Bayes Factors perform well in simulation studies, but a number of questions remains. Free University De Boelelaan 1105 1081 HV Amsterdam The Netherlands" @default.
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- W1544156720 date "1993-01-01" @default.
- W1544156720 modified "2023-09-25" @default.
- W1544156720 title "A fair comparison between regression models of different dimension" @default.
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