Multivariate-multiple circular regression. Issue 7 (3rd May 2017)

• Record Type: Journal Article
• Authors: Kim, Sungsu; SenGupta, Ashis
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 7(2017)
• Page Start: 1277
• Page End: 1291
• Publication Date: 2017-05-03
• Subjects: Circular regression -- circular variable -- mean-square error -- multiple regression -- multivariate regression; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1261292
• Languages: English
• ISSNs: 0094-9655

Abstract

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.