### Abstract

Keywords: Fractional Bayes factor, Ockham’s razor, (In)equality constraints, Updated prior

Original language | English |
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Pages (from-to) | 448-463 |

Journal | Computational Statistics and Data Analysis |

Volume | 71 |

DOIs | |

Publication status | Published - 2014 |

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**Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses.** / Mulder, J.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses

AU - Mulder, J.

PY - 2014

Y1 - 2014

N2 - A new method is proposed for testing multiple hypotheses with equality and inequality constraints on the parameters of interest. The method is based on the fractional Bayes factor with a modification that the updated prior is centered on the boundary of the constrained parameter space under investigation. The resulting prior adjusted default Bayes factors work as an “Ockham’s razor” when testing inequality constrained hypotheses, which is not the case for the fractional Bayes factor. Two different types of prior adjusted default Bayes factors are considered. In the first type, the updated prior is based on imaginary training data. Analytical and numerical examples show that this criterion converges fastest to a true inequality constrained hypothesis. In the second type, the updated prior is based on empirical training data. This second criterion only outperforms the fractional Bayes factor in the case of small samples.Keywords: Fractional Bayes factor, Ockham’s razor, (In)equality constraints, Updated prior

AB - A new method is proposed for testing multiple hypotheses with equality and inequality constraints on the parameters of interest. The method is based on the fractional Bayes factor with a modification that the updated prior is centered on the boundary of the constrained parameter space under investigation. The resulting prior adjusted default Bayes factors work as an “Ockham’s razor” when testing inequality constrained hypotheses, which is not the case for the fractional Bayes factor. Two different types of prior adjusted default Bayes factors are considered. In the first type, the updated prior is based on imaginary training data. Analytical and numerical examples show that this criterion converges fastest to a true inequality constrained hypothesis. In the second type, the updated prior is based on empirical training data. This second criterion only outperforms the fractional Bayes factor in the case of small samples.Keywords: Fractional Bayes factor, Ockham’s razor, (In)equality constraints, Updated prior

U2 - 10.1016/j.csda.2013.07.017

DO - 10.1016/j.csda.2013.07.017

M3 - Article

VL - 71

SP - 448

EP - 463

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

ER -