Exponential Models for Directional Data

Abstract

A rotationally invariant exponential model, which includes the Fisher-von Mises and Bingham distributions as special cases, is proposed for directional data in $R^p(p \geqslant 2)$. A new regression estimator for the model parameters is developed as a competitor to the maximum likelihood estimator. Both the new estimator and the MLE are asymptotically efficient at the postulated model and are robust under small departures from that model. Computationally, the regression estimator is much simpler since it requires no iterations or numerical integrations. Goodness-of-fit can be assessed by fitting nested special cases of the general model to the data.