title: Bayesian evaluation of inequality constrained hypotheses.
abstract: Bayesian evaluation of inequality constrained hypotheses enables researchers to investigate their expectations with respect to the structure among model parameters. This article proposes an approximate Bayes procedure that can be used for the selection of the best of a set of inequality constrained hypotheses based on the Bayes factor in a very general class of statistical models. The software package BIG is provided such that psychologists can use the approach proposed for the analysis of their own data. To illustrate the approximate Bayes procedure and the use of BIG, we evaluate inequality constrained hypotheses in a path model and a logistic regression model. Two simulation studies on the performance of our approximate Bayes procedure show that it results in accurate Bayes factors.
authors.affiliation: Department of Methodology and Statistics.
authors.forename: Xin
authors.initials: X
authors.lastname: Gu
authors.affiliation: Department of Methodology and Statistics, Tilburg University.
authors.forename: Joris
authors.initials: J
authors.lastname: Mulder
authors.affiliation: Department of Clinical Child and Family Studies, Utrecht University.
authors.forename: Maja
authors.initials: M
authors.lastname: Deković
authors.affiliation: Department of Methodology and Statistics.
authors.forename: Herbert
authors.initials: H
authors.lastname: Hoijtink
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016015:Logistic Models; D010287:Parent-Child Relations; D012984:Software; D013223:Statistics as Topic