The Fisher-Bingham distribution is obtained when a multivariate normal random vector is conditioned to have unit length. Its normalising constant can be expressed as an elementary function multiplied by the density, evaluated at 1, of a linear combination of independent noncentral x2 1 random variables. Hence we may approximate the normalising constant by applying a saddlepoint approximation to this density. Three such approximations, implementation of each of which is straightforward, are investigated: the first-order saddlepoint density approximation, the second-order saddlepoint density approximation and a variant of the second-order approximation which has proved slightly more accurate than the other two. The numerical and theoretical results we present show that this approach provides highly accurate approximations in a broad spectrum of cases.
The offset-normal shape distribution is defined as the induced shape distribution of a gaussian distributed random configuration in the plane. Such distributions were introduced in Dryden and Mardia (1991) and represent an important parameterized family of shape distributions for shape analysis. This paper reports a method for performing maximum likelihood estimation of parameters involved. The method consists of an EM algorithm with simple update rules and is shown to be easily applicable in many practical examples. We also show the necessary adjustments needed for using this algorithm for shape regression, missing landmark data and mixtures of offsetnormal shape distributions.
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