A Generalization of Spectral Analysis with Application to Ranked Data

Abstract

An analog of the spectral analysis of time series is developed for data in general spaces. This is applied to data from an election in which 5738 people rank ordered five candidates. Group theoretic considerations offer an analysis of variance like decomposition which seems natural and fruitful. A variety of inferential tools are suggested. The spectral ideas are then extended to general homogeneous spaces such as the sphere.