Model selection of the generalized von Mises distribution based on empirical mode decomposition with data analyses

This paper presents a method for selecting a distribution within the generalized von Mises (GvM) class. In this method, the logarithmic form of the GvM probability frequency function is regarded as the sum of a constant and several cosine functions with different frequencies. Based on the empirical mode decomposition (EMD) method, the corresponding logarithmic series is decomposed to several intrinsic mode functions (IMF) whose corresponding instantaneous frequencies (IF) are used to be the basis of the GvM model selection. The applications of the proposed method are illustrated using simulated circular data and real wind direction data. The results demonstrate that the method proposed here can provide a good choice for the GvM model selection.