We compared maximum likelihood and the k-NN algorithm in the context of discriminant analysis with spherical data.
We present new model for analyzing compositional data with structural zeros. Inspired by Butler and Glasbey (2008) who suggested a model in the presence of zero values in the data we propose a model that treats the zero values in a different manner.
Discriminant analysis for spherical data, or directional data in general, has not been extensively studied, and most papers focus on one distribution, the von Mises-Fisher. In this work, we study more distributions, escaping the rotational symmetry bound of the aforementioned distribution and also include a non parametric classier, the k-NN algorithm.
A folded type model is developed for analyzing compositional data based that provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation.
Εγγραφείτε στην λίστα ειδοποιήσεων του Τμήματος Οικονομικών Επιστημών.