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The paper attempts to address the robustness issues in circular–circular regression. The Möbius transformation-based link function for circular–circular regression is considered. Defining the concept of breakdown point in this context, the robustness issues of the estimators in this model are discussed. Maximum trimmed cosine estimator in this context is considered and the breakdown point of the estimator is calculated. An exact polynomial time algorithm is then proposed for the computation of the estimator which makes the methodology useful and readily applicable for empirical datasets. Simulation studies show that the estimator is robust with respect to the outliers. An analysis of real data is performed to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

The von Mises distribution is widely used for modeling angular data. When such data are seen in a quality control setting, there may be interest in checking whether the values are in statistical control or have gone out of control. A cumulative sum (cusum) control chart has desirable properties for checking whether the distribution has changed from an in-control to an out-of-control setting. This paper develops cusums for a change in the mean direction and concentration of angular data and illustrates some of their properties. [ABSTRACT FROM AUTHOR]

The way of investigating a distribution knowing its interesting properties might be often inadequate when the shapes of two distributions are almost similar. In each of these circumstances, the accurate decision about the genesis of a random sample from any of the two parent distributions will be very much ambiguous even with the availability of the existing testing procedure of the circular data. A sequential discrimination procedure has been suggested which is also invariant to the sample size. The performance of the proposed discrimination procedure has been evaluated by checking its capability of detecting the genesis of the known samples from the two identically shaped wrapped distributions. [ABSTRACT FROM AUTHOR]

We propose a family of four-parameter distributions on the circle that contains the von Mises and wrapped Cauchy distributions as special cases. The family is derived by transforming the von Mises distribution via a Möbius transformation, which maps the unit circle onto itself. The densities in the family have a symmetric or asymmetric, unimodal or bimodal shape, depending on the values of the parameters. Conditions for unimodality are explored. Further properties of the proposed model are obtained, many by applying the theory of Möbius transformation. Properties of a three-parameter symmetric submodel are investigated as well; these include maximum likelihood estimation, its asymptotics, and a reparameterization that proves useful quite generally. A three-parameter asymmetric subfamily, which often proves to be an adequate model, is also discussed, with emphasis on its mean direction and circular skewness. The proposed family and subfamilies are used to model an asymmetrically distributed data set and are then adopted as the angular error distribution of a circular–circular regression model. Two applications of the latter are given. It is in this regression context that the Möbius transformation especially comes into its own. Comparisons with other families of circular distributions are made. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]