1. Assessing a Bayesian embedding approach to circular regression models
- Author
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Cremers, J., Mainhard, M.T., Klugkist, I.G., Leerstoel Klugkist, Leerstoel van Gog, Methodology and statistics for the behavioural and social sciences, and Education and Learning: Development in Interaction
- Subjects
Model checking ,Bayesian methods ,Computer science ,Bayesian probability ,computer.software_genre ,01 natural sciences ,010104 statistics & probability ,symbols.namesake ,interpersonal circumplex ,Bayesian multivariate linear regression ,0101 mathematics ,General Psychology ,Statistical hypothesis testing ,circular data ,business.industry ,05 social sciences ,050301 education ,General Social Sciences ,Markov chain Monte Carlo ,Regression analysis ,Pattern recognition ,symbols ,Embedding ,regression ,Artificial intelligence ,Data mining ,Bayesian linear regression ,business ,0503 education ,computer - Abstract
Abstract. Circular data is different from linear data and its analysis also requires methods different from conventional methods. In this study a Bayesian embedding approach to estimating circular regression models is investigated, by means of simulation studies, in terms of performance, efficiency, and flexibility. A new Markov chain Monte Carlo (MCMC) sampling method is proposed and contrasted to an existing method. An empirical example of a regression model predicting teachers’ scores on the interpersonal circumplex will be used throughout. Performance and efficiency are better for the newly proposed sampler and reasonable to good in most situations. Furthermore, the method in general is deemed very flexible. Additional research should be done that provides an overview of what circular data looks like in practice, investigates the interpretation of the circular effects and examines how we might conduct a way of hypothesis testing or model checking for the embedding approach.
- Published
- 2018