Tsagris and Alzeley proposed the generalized circular projected Cauchy (GCPC) distribution, whose special case is the wrapped Cauchy distribution. In this paper we first derive the relationship with the wrapped Cauchy distribution, and then we attempt to characterize the distribution. We establish the conditions under which the distribution exhibits unimodality. We provide non-closed-form expressions for the mean resultant length and the Kullback–Leibler divergence and analytical forms for the cumulative probability function and the entropy of the GCPC distribution. We propose log-likelihood ratio tests for one- and two-location parameters without assuming the equality of the concentration parameters. We revisit maximum likelihood estimation with and without predictors. In the regression setting we briefly discuss the addition of circular and simplicial predictors. Simulation studies illustrate (a) the performance of the log-likelihood ratio test when one falsely assumes that the true distribution is the wrapped Cauchy distribution, and (b) the empirical rate of convergence of the regression coefficients. Using a real data example, we show how to avoid the log-likelihood being trapped in a local maximum, and we correct a mistake in the regression setting.
Alzeley et al. (Tue,) studied this question.