A semiparametric bayesian model for circular-linear regression

Document Type

Article

Publication Date

12-1-2006

Abstract

Circular data are observations that are represented as points on a unit circle. Times of day and directions of wind are two such examples. In this work, we present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is useful especially when the likelihood surface is ill behaved. Markov chain Monte Carlo techniques are used to fit the proposed model and to generate predictions. The method is illustrated using an environmental data set.

Identifier

33750214670 (Scopus)

Publication Title

Communications in Statistics Simulation and Computation

External Full Text Location

https://doi.org/10.1080/03610910600880302

e-ISSN

15324141

ISSN

03610918

First Page

911

Last Page

923

Issue

4

Volume

35

Fund Ref

U.S. Environmental Protection Agency

This document is currently not available here.

Share

COinS