Bayes Factors for Comparison of Two-Way ANOVA Models
- DOI
- 10.2991/jsta.d.201230.001How to use a DOI?
- Keywords
- ANOVA model; Bayes factor; Zellner's g prior; Jeffrey-Zellner-Siow prior; Hyper-g prior; Simulation data
- Abstract
In the traditional two-way analysis of variance (ANOVA) model, it is possible to identify the significance of both the main effects and their interaction based on the P values. However, it is not possible to determine how much data supports the model when these effects are incorporated into the model. To overcome this practical difficulty, we applied Bayes factors for hierarchical models to check the intensity of the effects (both main and interaction). The objective is to identify the impact of the main and interaction effects based on a comparison of Bayes factors of the hierarchical ANOVA models. The application of Bayes factors enables to observe which model strengthens more while including or eliminating the effects in the model. Consequently, this paper proposes three priors such as Zellner's g, Jefferys-Zellner-Siow, and Hyper-g priors, to compute the Bayes factor. Finally, we extended this procedure to the simulation data for the generalization of the Bayesian results.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - R. Vijayaragunathan AU - M. R. Srinivasan PY - 2021 DA - 2021/01/05 TI - Bayes Factors for Comparison of Two-Way ANOVA Models JO - Journal of Statistical Theory and Applications SP - 540 EP - 546 VL - 19 IS - 4 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.201230.001 DO - 10.2991/jsta.d.201230.001 ID - Vijayaragunathan2021 ER -