Multivariate-multiple circular regression
Article Type
Research Article
Publication Title
Journal of Statistical Computation and Simulation
Abstract
We introduce a fully model-based approach of studying functional relationships between a multivariate circular-dependent variable and several circular covariates, enabling inference regarding all model parameters and related prediction. Two multiple circular regression models are presented for this approach. First, for an univariate circular-dependent variable, we propose the least circular mean-square error (LCMSE) estimation method, and asymptotic properties of the LCMSE estimators and inferential methods are developed and illustrated. Second, using a simulation study, we provide some practical suggestions for model selection between the two models. An illustrative example is given using a real data set from protein structure prediction problem. Finally, a straightforward extension to the case with a multivariate-dependent circular variable is provided.
First Page
1277
Last Page
1291
DOI
10.1080/00949655.2016.1261292
Publication Date
5-3-2017
Recommended Citation
Kim, Sungsu and SenGupta, Ashis, "Multivariate-multiple circular regression" (2017). Journal Articles. 2574.
https://digitalcommons.isical.ac.in/journal-articles/2574