A Bayesian analysis of the change-point problem for directional data. (English) Zbl 1147.62024

Summary: We discuss a simple fully Bayesian analysis of the change-point problem for directional data in the parametric framework with von Mises or circular normal distributions as the underlying distribution. We first discuss the problem of detecting change in the mean direction of the circular normal distribution using a latent variable approach when the concentration parameter is unknown. Then, a simpler approach, beginning with proper priors for all the unknown parameters - the sampling importance resampling technique - is used to obtain the posterior marginal distribution of the change-point.
The method is illustrated using wind data [E. P. Weijers, A. Van Delden, H. F. Vugts and A. G. C. A. Meesters, The composite horizontal wind field within convective structures of the atmospheric surface layer. J. Atmos. Sci. 52, 3866–3878 (1995)]. The method can be adapted for a variety of situations involving both angular and linear data and can be used with profit in the context of statistical process control in Phase I of control charting and also in Phase II in conjunction with control charts.


62F15 Bayesian inference
62H11 Directional data; spatial statistics
62P12 Applications of statistics to environmental and related topics
86A10 Meteorology and atmospheric physics


Full Text: DOI


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