  The parameter space of nonnegative trigonometric sums (NNTS) models for
circular data is the surface of a hypersphere; thus, constructing regression
models for a circular-dependent variable using NNTS models can comprise fitting
great (small) circles on the parameter hypersphere that can identify different
regions (rotations) along the great (small) circle. We propose regression
models for circular- (angular-) dependent random variables in which the
original circular random variable, which is assumed to be distributed
(marginally) as an NNTS model, is transformed into a linear random variable
such that common methods for linear regression can be applied. The usefulness
of NNTS models with skewness and multimodality is shown in examples with
simulated and real data.
