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A Bayesian analysis of the change-point problem for directional data

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  • Ashis Sengupta
  • Arnab Kumar Laha

Abstract

In this paper, we discuss a simple fully Bayesian analysis of the change-point problem for the directional data in the parametric framework with von Mises or circular normal distribution 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 the 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 (1995. 3866–3878]. 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.

Suggested Citation

  • Ashis Sengupta & Arnab Kumar Laha, 2008. "A Bayesian analysis of the change-point problem for directional data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 693-700.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:693-700
    DOI: 10.1080/02664760801924004
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    Cited by:

    1. Douglas M. Hawkins & F. Lombard, 2017. "Cusum control for data following the von Mises distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1319-1332, June.

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