Bain: a program for Bayesian testing of order constrained hypotheses in structural equation models

Xin Gu; Herbert Hoijtink; Joris Mulder; Yves Rosseel

doi:10.1080/00949655.2019.1590574

Bain: a program for Bayesian testing of order constrained hypotheses in structural equation models

Xin Gu^*, Herbert Hoijtink, Joris Mulder, Yves Rosseel

^*Corresponding author for this work

Ghent University

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

Abstract

This paper presents a new statistical method and accompanying software for the evaluation of order constrained hypotheses in structural equation models (SEM). The method is based on a large sample approximation of the Bayes factor using a prior with a data-based correlational structure. An efficient algorithm is written into an R package to ensure fast computation. The package, referred to as Bain, is easy to use for applied researchers. Two classical examples from the SEM literature are used to illustrate the methodology and software.

Original language	English
Pages (from-to)	1526-1553
Number of pages	28
Journal	Journal of Statistical Computation and Simulation
Volume	89
Issue number	8
DOIs	https://doi.org/10.1080/00949655.2019.1590574
Publication status	Published - 24 May 2019

Funding

Herbert Hoijtink is supported by the Netherlands Organization for Scientific Research (NWO grant number 024.001.003). Joris Mulder was supported by a Vidi Grant of the Netherlands Organization of Scientific Research (NWO grant number 452-17-006).

Keywords

Approximate Bayesian procedure
Bayes factors
order constrained hypothesis
structural equation model

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10.1080/00949655.2019.1590574

BainFinal published version, 2.27 MBLicence: Taverne

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Bain: a program for Bayesian testing of order constrained hypotheses in structural equation models

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