TY - JOUR
T1 - Assessing a Bayesian Embedding Approach to Circular Regression Models
AU - Cremers, Jolien
AU - Mainhard, Tim
AU - Klugkist, Irene
N1 - Funding Information:
This work was supported by a Vidi grant awarded to I. Klugkist from the Dutch Organization for Scientific Research (NWO 452-12-010).
Publisher Copyright:
© 2018 Hogrefe Publishing.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Bayesian methods
KW - Circular data
KW - interpersonal circumplex
KW - regression
UR - http://www.scopus.com/inward/record.url?scp=85049162218&partnerID=8YFLogxK
U2 - 10.1027/1614-2241/a000147
DO - 10.1027/1614-2241/a000147
M3 - Article
AN - SCOPUS:85049162218
SN - 1614-1881
VL - 14
SP - 69
EP - 81
JO - Methodology
JF - Methodology
IS - 2
ER -